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Display Omitted This paper puts forward ICA for allocating FACTS devices.TCPST and TCSC are used to relieve consequences of line outage and increased demand.The results show that ICA efficiently solves TCPST and TCSC allocation problems.The results show that FACTS devices drastically enhance power system static security.The results approve the outperformance of ICA over some state of the art algorithms. The problem of optimal allocation of flexible AC transmission systems (FACTS) devices is deemed as a formidable optimisation problem. Metaheuristics are the common approaches for solving FACTS allocation problems. Imperialistic competitive algorithm (ICA) is a well-established optimisation algorithm which has been successfully employed for solving complex optimisation problems in different fields. It is inspired by imperialistic competition and socio-political evolution of human beings and offers appropriate exploration and exploitation capabilities during the search for global optima. This paper employs ICA for solving FACTS allocation problem in a way that low values of overloads and voltage deviations are resulted both during line outage contingencies and demand growth. Thyristor-controlled phase shifting transformers (TCPST's) and thyristor-controlled series compensators (TCSC's) have been used as FACTS devices. The results of employing ICA for FACTS allocation problem indicate that ICA Offers better results than artificial bee colony (ABC), gravitational search algorithm (GSA), evolutionary programming (EP), bat swarm optimisation (BSO), nonlinear programming (NLP), pattern search (PS), asexual reproduction optimisation (ARO) and backtracking search algorithm (BSA).

HighlightsA novel re-formulation of quadrotor dynamics leads to a cascaded form that allows the use of a backstepping controller and results in a more disturbance tolerant control as the use of algebraic inversion is avoided.Partial contraction is used to derive the backstepping control that guarantees stability and convergence.It is shown conclusively that the full state backstepping controller is more superior than the inverse kinematics-based backstepping controller in terms of unmodeled effects rejection.The backstepping controller formulated by the contraction theory in this paper is generic and can be applied to any dynamical systems satisfying the cascaded structure.Extensive simulation studies have been carried out to include aerodynamic effects and wind disturbances, and the proposed controller is verified to perform better than the benchmark controller. In this paper, a tracking controller is formulated for a quadrotor to track a moving ground target. The quadrotor exhibits distinct hierarchical dynamics that allows its position to be controlled by its attitude. This motivates the use of backstepping control on the underactuated quadrotor. Most backstepping architecture controls the quadrotor position and attitude independently, and couples them with inverse kinematics. Inverse kinematics computes the attitude angles required to achieve a desired acceleration. However unmodeled effects are shown to cause inexact inversion resulting in tracking error. The approach proposed in this paper uses a re-formulated full state cascaded dynamics to eliminate the need for inverse kinematics in a full state backstepping control architecture. It is shown that zero steady state error is achieved in the presence of unmodeled aerodynamics effect and wind disturbance despite no integral action. In addition, a backstepping formulation is derived using contraction theory that guarantees the boundedness of state response under bounded disturbances such as wind. This improves the system performance. Numerical simulations are performed using the proposed controller to track a target moving along predefined paths and the results are compared with a benchmark controller derived using inverse kinematics. The results show that the proposed controller is able to achieve better tracking performance under unmodeled aerodynamic effects and wind disturbance as compared with the benchmark controller.

Average deviation from optimal solution.Display Omitted The model of Integrated Due-date Assignment and Production and Outbound Distribution Scheduling is addressed.Two meta-heuristic algorithms including Adaptive Genetic Algorithm and Parallel Simulated Annealing and one heuristic algorithm are developed.In AGA, six new crossover and mutation operators and in PSA one new neighborhood generation are introduced.A full factorial experimental design is used to test the performance of meta-heuristic algorithms.Computational results show the better performance of AGA. In production systems, manufacturers face important decisions that affect system profit. In this paper, three of these decisions are modelled simultaneously: due date assignment, production scheduling, and outbound distribution scheduling. These three decisions are made in the sales, production planning and transportation departments. Recently, many researchers have devoted attention to the problem of integrating due date assignment, production scheduling and outbound distribution scheduling. In the present paper, the problems of minimizing costs associated with maximum tardiness, due date assignment and delivery for a single machine are considered. Mixed Integer Non-Linear Programming (MINLP) and a Mixed Integer Programming (MIP) are used for the solution. This problem is NP-hard, so two meta-heuristic algorithms, an Adaptive Genetic Algorithm (AGA) and a Parallel Simulated Annealing algorithm (PSA), are used for solution of large-scale instances. The present paper is the first time that crossover and mutation operators in AGA and neighbourhood generation in PSA have been used in the structure of optimal solutions. We used the Taguchi method to set the parameters, design of experiments (DOE) to generate experiments, and analysis of variance, the Friedman, Aligned Friedman, and Quade tests to analyse the results. Also, the robustness of the algorithms was addressed. The computational results showed that AGA performed better than PSA.

Graphical abstractThe contribution of this paper is to develop a new stochastic search strategy which is specific for hybrid global optimization. Numerical experiments show that this method achieves better performance those many compared existing algorithms. Display Omitted HighlightsWe proposed a novel stochastic search method, called Greedy Diffusion Search (GDS). It holds the ability to escape from local minima for multi modal problems.Combining GDS with limited memory BFGS, we propose a hybrid global optimization method to solve constrained optimization problems.To evaluate the effectiveness of the proposed algorithm, some benchmark problems as well as some examples from the literature are tested. The outcomes are highly satisfactory. In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to.

Description of the states in both before updating and after updating.Figure gives an example to illustrate our algorithm. Part (a) gives the state of before updating. Part (b) explains the state of after updating. Assume there are five peaks in the search space, numbered 1-5 in Part (b). However, only four peaks are detected by the current population, numbered 1-4 in Part (a).Where, ź denotes a replacer from the memory, Δ denotes a new generated particle around the replacer, and ź denotes a replaced particle in the population. We can see that no more than one particle is replaced in each sub-population.Since ź in peak 5 is better than the closest seed, i.e. the seed in sub-population 2, and its distance to the closest seed is larger than rs, the sub-population 5 is created to exploit this area.In sub-population 3, ź is better than the closest seed, i.e. the seed in sub-population 3, and the distance between it to the closest seed is larger than 0.5×rs and less than rs, so only one Δ is created around the ź.In sub-population 2, ź is better than the closest seed, i.e. the seed in sub-population 2, but the distance between it to the closest seed is less than 0.5×rs, so only ź is added to this sub-population.There is one case that has not been described in this example. Here we suppose the replacer is not better than the worst particle in sub-population 1, so no replacement is conducted in this sub-population.Display Omitted A new population updating method is proposed to enhance a representative algorithm, i.e. the Species-based Particle Swarm Optimization.Experimental results show that the MSPSO is competitive on MPB, CMPB and DRGDB.The effect of the memory size on the performance of the proposed algorithm is tested. Both the species strategy and the memory scheme are efficient methods for addressing dynamic optimization problems. However, the combination of these two efficient techniques has scarcely been studied. Thus, this paper focuses on how to hybridize these two methods. In this paper, a new swarm updating method is proposed to enhance a representative species-based algorithm, i.e., SPSO (Species-based Particle Swarm Optimization), and the new algorithm is named MSPSO. MSPSO has two characteristics. First, the number of replaced particles in the current swarm is set adaptively according to the number of species. To not substantially destroy the exploitation capability of each species, no more than one particle in each species is replaced by the memory. Second, the retrieved memory particles are categorized according to their fitness values and their distances to the seed of the closest species. Aimed at enhancing the search in both promising areas and existing species, each category is processed by different operations. The MPB, Cyclic MPB and DRPBG are used to test the performance of MSPSO. Experimental results demonstrate that MSPSO is competitive for dynamic optimization problems.

Display OmittedA complete flowchart routine of energy curve based multilevel image thresholding. Energy curve based colour multilevel thresholding has been proposed.CS and ELR-CS based optimization techniques have been exploited.Different objective functions have been utilized for optimum results.CS-Kapur's found giving better results. Amongst all the multilevel thresholding techniques, standard histogram based thresholding approaches are very impressive for bi-level thresholding. But, it is not effective to select spatial contextual information of the image for choosing optimal thresholds. In this paper, a new color image thresholding technique is presented by using an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The property of this energy curve is very much similar to histogram of the image. To estimate the spatial contextual information for thresholding practice, in place of histogram, the energy curve function is used as an input. A new energy curve based color image segmentation approach using three well known objective functions named Kapur's entropy, between-class-variance, and Tsalli's entropy is proposed. In this paper, cuckoo search (CS) and egg lying radius-cuckoo search (ELR-CS) optimization algorithms with different parameter analysis have been used for solving the color image multilevel thresholding problem. The experimental results demonstrate that the proposed CS-Kapur's energy curve based segmentation can powerfully and accurately search the multilevel thresholds.

Display Omitted This paper investigates the use of different methods for estimating the probability distributions used in the update rule of Bare Bones PSO.Four updating strategies are introduced for updating particle positions in Bare Bones PSO that are using normal or multivariate normal distributions.A novel covariance matrix adaption scheme is introduced for updating the covariance matrix of the multivariate Gaussian distribution.A strategy selection mechanism based on cellular learning automata is proposed for the selection of an appropriate updating strategy based on the problem properties and evolutionary stage of the algorithm's execution.In order to demonstrate the characteristics of the proposed method, comparisons with some other PSO algorithms from the literature are performed. Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. However, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense impact on the performance of the bare bones PSO. As a result, this paper investigates the use of different methods for estimating the probability distributions used in the update rule. Four methods or strategies are developed that are using Gaussian or multivariate Gaussian distributions. The choice of an appropriate updating strategy for each particle greatly depends on the characteristics of the fitness landscape that surrounds the swarm. To deal with issue, the cellular learning automata model is incorporated with the proposed bare bones PSO, which is able to adaptively learn suitable updating strategies for the particles. Through the interactions among its elements and the learning capabilities of its learning automata, cellular learning automata gradually learns to select the best updating rules for the particles based on their surrounding fitness landscape. This paper also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies. The proposed method is compared with some other well-known particle swarm approaches. The results indicate the superiority of the proposed approach in terms of the accuracy of the achieved results and the speed in finding appropriate solutions.

The optimization of the antecedent parameters for a type 2 fuzzy system of edge detection is presented.The goal of interval type-2 fuzzy logic in edge detection methods is to provide the ability to handle uncertainty.Results show that the Cuckoo search provides better results in optimizing the type-2 fuzzy system. This paper presents the optimization of a fuzzy edge detector based on the traditional Sobel technique combined with interval type-2 fuzzy logic. The goal of using interval type-2 fuzzy logic in edge detection methods is to provide them with the ability to handle uncertainty in processing real world images. However, the optimal design of fuzzy systems is a difficult task and for this reason the use of meta-heuristic optimization techniques is also considered in this paper. For the optimization of the fuzzy inference systems, the Cuckoo Search (CS) and Genetic Algorithms (GAs) are applied. Simulation results show that using an optimal interval type-2 fuzzy system in conjunction with the Sobel technique provides a powerful edge detection method that outperforms its type-1 counterparts and the pure original Sobel technique.

Graphical abstractDisplay Omitted In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.

Graphical abstractDisplay Omitted HighlightsThis paper presents an automatic system based on biomarker for assessing water quality.The proposed system utilized case-based reasoning for indicating the degree of water quality.Water quality is classified based on the histopathological changes in fish gills and liver.Performance evaluation metrics are retrieval accuracy, receiver operating receiver (ROC) curves, F-measure, and G-mean.That proposed CBR based system has obtained water quality classification accuracy of 97.9%. Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia "Oreochromis niloticus" fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate - Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.

Graphical abstractDisplay Omitted This paper presents a novel approach for multiclass classification by fusion of KAZE and Scale Invariant Feature Transform (SIFT) features followed by Minimal Complexity Machine (MCM) as the classifier. Unlike the existing features, the paper proposes a new feature SIKA to represent characteristics of an object, as opposed to just forming a compendium of interest points in an image to represent the object characteristics. Further we append a strong and lightweight classifier, MCM to the technique. The resulting classifier easily outperforms existing techniques based on handcrafted features. Two new scores Keypoint Overlap Score (KOS) and Mean Keypoint Overlap Score (MKOS) have also been proposed as part of this work which are useful in establishing the strength of features for object representation.

Display Omitted A review of VIKOR and its fuzzy extensions on applications is presented.The systematic classification covers 13 different application areas.It provides an insight for researchers and practitioners on VIKOR applications. Multi criteria decision making (MCDM) is one of the research areas of operations research and management science which has widely studied by researchers and practitioners. It finds a compromise solution for evaluating and ranking alternatives from the best to the worst under conflicting criteria with respect to decision maker(s) preferences. In a compromise approach, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR; that means multi-criteria optimization and compromise solution) continues to be applied satisfactorily across different application areas. This paper conducts a state-of-the-art literature review to categorize, analyze and interpret the current research on VIKOR applications. It also discusses the extensions of VIKOR applied in fuzzy environments. A total of 343 papers are classified into 13 different application areas and a number of sub-application areas. Furthermore, all papers are also categorized with respect to publication year, published journal, country of origin, application type (real case study vs empirical study), and version of fuzzy sets used. This comprehensive literature review provides an insight for researchers and practitioners on VIKOR applications in terms of showing current state and potential areas for future attempts to be focused in the future.

Graphical abstractDisplay Omitted The multi-level thresholding is a popular method for image segmentation. However, the method is computationally expensive and suffers from premature convergence when level increases. To solve the two problems, this paper presents an advanced version of gravitational search algorithm (GSA), namely hybrid algorithm of GSA with genetic algorithm (GA) (GSA-GA) for multi-level thresholding. In GSA-GA, when premature convergence occurred, the roulette selection and discrete mutation operators of GA are introduced to diversify the population and escape from premature convergence. The introduction of these operators therefore promotes GSA-GA to perform faster and more accurate multi-level image thresholding. In this paper, two common criteria (1) entropy and (2) between-class variance were utilized as fitness functions. Experiments have been performed on six test images using various numbers of thresholds. The experimental results were compared with standard GSA and three state-of-art GSA variants. Comparison results showed that the GSA-GA produced superior or comparative segmentation accuracy in both entropy and between-class variance criteria. Moreover, the statistical significance test demonstrated that GSA-GA significantly reduce the computational complexity for all of the tested images.

Display Omitted In this paper a novel bipolar framework is proposed for improving efficiency in Cloud's resource provisioning.The proposed bipolar framework enables the cloud provider to provide resources for clients' requests proactively based on demands rather than conventional workload based predictive resource allocation.Artificial neural networks are employed to provide anticipation for demand. Cloud's profitability is mainly driven by the business, and on the other hand, a successful business is hardly geared with clients' satisfaction. Therefore, there is high competition between cloud providers for satisfying clients and attracting more of them. In this way, long term business success factors should also be considered in addition to short term profit factors regarded in conventional resource provisioning procedures. Conventional resource management approaches to achieve short term profit inevitably lead to job rejection and violation from response time based SLAs while short response time and low job rejection are of those important factors to clients' satisfaction. Therefore, this paper proposes a novel bipolar resource management framework which results in preventing from job rejection and having considerably reduced violations from response time based SLAs as well as providing short term profits. The proposed framework uses a neural network based predictor and genetic algorithm for optimal resource management through live migration. It also employs a prediction based temporal infinite pool, called the temporal cloud, which regards job rejection prevention. The evaluation of the proposed framework demonstrates that it can provide short term profits, beside it prevents from job rejection and reduces response time violations considerably.

Graphical abstractThe effects of origin bias on the Teaching-Learning-Based-Optimization algorithm. Display Omitted HighlightsA geometric interpretation is applied to explain population convergence.A property of TLBO introduces an origin bias in the Teacher Phase.A converged population will continue searching in the direction of the origin for better solutions.The origin bias is directly tied to convergence.A faster rate of convergence yields a higher success rate for problematic functions. Teaching-Learning-Based-Optimization (TLBO) is a population-based Evolutionary Algorithm which uses an analogy of the influence of a teacher on the output of learners in a class. TLBO has been reported to obtain very good results for many constrained and unconstrained benchmark functions and engineering problems. The choice for TLBO by many researchers is partially based on the study of TLBO's performance on standard benchmark functions. In this paper, we explore the performance on several of these benchmark functions, which reveals an inherent origin bias within the Teacher Phase of TLBO. This previously unexplored origin bias allows the TLBO algorithm to more easily solve benchmark functions with higher success rates when the objective function has its optimal solution as the origin. The performance on such problems must be studied to understand the performance effects of the origin bias. A geometric interpretation is applied to the Teaching and Learning Phases of TLBO. From this interpretation, the spatial convergence of the population is described, where it is shown that the origin bias is directly tied to spatial convergence of the population. The origin bias is then explored by examining the performance effect due to: the origin location within the objective function, and the rate of convergence. It is concluded that, although the algorithm is successful in many engineering problems, TLBO does indeed have an origin bias affecting the population convergence and success rates of objective functions with origin solutions. This paper aims to inform researchers using TLBO of the performance effects of the origin bias and the importance of discussing its effects when evaluating TLBO.

Membrane algorithmDisplay Omitted This paper proposes a new membrane algorithm based on rules of PSO defined within the framework of cell-like P-systems to solve Sudoku.A novel way is introduced to define the search space for solving the problem of Sudoku.Using the proposed algorithm the success rate for Sudoku puzzles of order 3 of "easy", "medium" and "hard" level is 100%.For "Evil" puzzles the success rate is however only 93% but it is the best ever recorded by any algorithm using PSO rules, as per the knowledge of the authors.Also, Sudoku of higher order is solved for which the success rate is about 87%. Sudoku, of order n, is a combinatorial puzzle having partially filled n2?n2 grid consisting of sub-grids of n?n dimension. In this paper, a new membrane algorithm, namely MA_PSO_M, is presented. It uses the modified rules of Particle Swarm Optimization coupled with a carefully designed mutation operator within the framework of cell-like P-systems. Another significant contribution of this paper is the novel way in which the search space for solving the Sudoku problem is defined. Initially, the proposed algorithm is used to solve Sudoku puzzles of order 3 available in literature. On the basis of experiments performed on sample Sudoku puzzles of 'easy' and 'medium' difficulty levels it is concluded that the proposed membrane algorithm, MA_PSO_M, is very efficient and reliable. For the 'hard' and 'evil' difficultly levels, too the algorithm performs very well after incorporating an additional deterministic phase. The performance of the algorithm is further enhanced with an increased population size in a very small computational time. To further demonstrate efficiency of algorithm it is applied to Sudoku puzzles of order 4. The obtained results prove that the proposed membrane algorithm clearly dominates any of the PSO based membrane algorithm existing in the literature.

Display Omitted Multicriteria decision analysis based on interval-valued intuitionistic fuzzy sets.Development of a projection-based compromising method.Establishment of projection-based compromising indices and comprehensive indices.Determination of the ranking order and the approval status of alternatives.Comparative analyses with other relevant compromising decision-making methods. The purpose of this paper is to develop a projection-based compromising method for addressing multiple criteria decision-making problems based on interval-valued intuitionistic fuzzy sets. The concept of projections considers not only the distance but also the included angle between evaluative ratings of alternative actions with respect to a criterion. In the interval-valued intuitionistic fuzzy context, this paper determines the respective projections of the evaluative ratings of each alternative on the positive-ideal and negative-ideal solutions and explores several essential properties. Next, this paper introduces the concepts of projection-based compromising indices and comprehensive compromising indices and further investigates relevant theorems for supporting the usefulness of these indices. Additionally, this paper proposes the projection-based comparative index and the comprehensive comparative index to serve as benchmark values for the comparison purpose. The improvement percentage of the comprehensive compromising value is acquired to determine the priority order of the alternatives, including the complete ranking order and the approval status for each alternative. The feasibility and the applicability of the proposed method are validated with an application problem of watershed site selection. Finally, several comparative analyses are conducted to verify the effectiveness and advantages of the proposed method over other relevant compromising decision-making methods.

Display Omitted A review regarding application of MCDM techniques in E-learning is conducted.Five major databases have been considered for this review.ScienceDirect, Emerald, Taylor and Francis, IEEE, and Springer have been selected.A systematic and meta analysis methodology called "PRISMA" has been proposed.Papers between 2001 and 2015 have been considered for this review. Academic research on E-learning has increased extensively over the past few years. Although, many multi-criteria decision making methods have been proposed to evaluate and examine the effectiveness of E-learning, there is a lack of study concerning systematic literature review and classification of research in this area. Regarding this, five major databases including ScienceDirect, Emerald, Taylor and Francis, IEEE, and Springer have been selected and a systematic methodology proposed. Consequently, a review of 42 published papers appearing in 33 academic journals and international conferences between 2001 and 2015 have been obtained to achieve a comprehensive review of MCDM application in E-learning. Accordingly, the selected papers have been classified by the year of publication, MCDM techniques, and journals and conferences in which they appeared. In addition, the significant criteria in evaluating E-learning were found. This study supports researchers and practitioners in effectively adopting MCDM techniques regarding E-learning evaluation and provides an insight into its state-of-the-art.

Display Omitted Novel cluster-space controller for non-holonomic systems based Behavioral Adaptive Fuzzy tuning algorithm (BAFC).Controller is easy to implement and performs tasks based on their importance.Fuzzy tuned controller uses position error and its rate of change as inputs.Implementation using SIMULINK on Lego EV3 WMRs wheeled robot is presented.Wi-Fi link establishes communication between agents and controller. Cooperation between autonomous robot vehicles holds several promising advantages like robustness, adaptability, configurability, and scalability. Coordination between the different robots and the individual relative motion represent both the main challenges especially when dealing with formation control and maintenance. Cluster space control provides a simple concept for controlling multi-agent formation. In the classical approach, formation control is the unique task for the multi-agent system. In this paper, the development and application of a novel Behavioral Adaptive Fuzzy-based Cluster Space Control (BAFC) to non-holonomic robots is presented. By applying a fuzzy priority control approach, BAFC deals with two conflicting tasks: formation maintenance and target following. Using priority rules, the fuzzy approach is used to adapt the controller and therefore the behavior of the system, taking into accounts the errors in the formation states and the target following states. The control approach is easy to implement and has been implemented in this paper using SIMULINK real-time platform. The communication between the different agents and the controller is established through Wi-Fi link. Both simulation and experimental results demonstrate the behavioral response where the robot performs the higher priority tasks first. This new approach shows a great performance with a lower control signal when benchmarked with previously known results in the literature.

Display Omitted This paper studies the order acceptance and scheduling problem with class setups in a single machine environment.An enhanced artificial bee colony algorithm is proposed for solving this problem.Experimental results reveal that the proposed algorithm is both computationally efficient and effective. This paper considers the order acceptance and scheduling problem in a single machine environment where each customer order is characterized by a known processing time, due date, revenue, and class setup time. The objective is to maximize the total revenue. Since the problem is computationally intractable, we first conduct a preliminary study of applying the basic artificial bee colony algorithm to address the problem under study. Specifically, we design appropriate neighborhood operators with respect to the problem. Based on the results of the preliminary study and the problem characteristics, an enhanced artificial bee colony algorithm is developed with a series of modifications. The extensive experimental results indicate that the enhanced artificial bee colony algorithm is both computationally efficient and effective for large sized problem instances.

Display Omitted This paper presents an approach for breast cancer diagnosis in digital mammograms using shearlet transform.A new approach is represented for feature extraction using shearlet transform, t-test statistic and dynamic thresholding.The system uses two datasets (ROIs) obtained from two different database; MIAS and DDSM database.The datasets include all abnormalites for breast cancer.The classification section was performed to distinguish the ROIs as normal-abnormal and benign-malignant. In this paper, I introduce a new method for feature extraction to classify digital mammograms using fast finite shearlet transform. Initially, fast finite shearlet transform was performed over mammogram images, and feature vectors were built using coefficients of the transform. In subsequent calculations, features were ranked according to t-test statistics, and capabilities were distinguished between different classes. To maximize differences between class representatives, a thresholding process was implemented as a final stage of feature extraction, and classifications were calculated over the optimal feature set using 5-fold cross validation and a support vector machine (SVM) classifier. The present results show that the proposed method provides satisfactory classification accuracy.

Load dispatch is developed by thermal and wind units with its uncertainties.Consider many objective functions into a new multi objective method.Propose a new decision making method to select best Pareto front.Propose some new operators for local and global searches.Propose a new online training mechanism for self adaptive goals. This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and wind units so that minimized the total generating costs are considered as multi objective model. Normally, the nature of the wind energy as a renewable energy sources has uncertainty in generation. Therefore, in this paper, use a practical model known as 2m-point to estimate the uncertainty of wind power. To solve the WEED problem, this paper proposed a new meta-heuristic optimization algorithm that uses online learning mechanism. Honey Bee Mating Optimization (HBMO), a moderately new population-based intelligence algorithm, shows fine performance on optimization problems. Unfortunately, it is usually convergence to local optima. Therefore, in the proposed Online Learning HBMO (OLHBMO), two neural networks are trained when reached to the predefined threshold by current and previous position of solutions and their fitness values. Moreover, Chaotic Local Search (CLS) operator is use to develop the local search ability and a new data sharing model determine the set of non-dominated optimal solutions and the set of non-dominated solutions to kept in the external memory. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a decision-making technique is employed to find the best solution from the set of Pareto solutions. The proposed model has been individually examined and applied on the IEEE 30-bus 6-unit, the IEEE 118-bus 14-unit, and 40-unit with valve point effect test systems. The robustness and effectiveness of this algorithm is shows by these test systems compared to other available algorithms.

Graphical abstractDisplay Omitted This paper presents a maiden application of EMA to solve power system ORPD problems.Steps of implementation of EMA to solve ORPD are elaborately discussed.The performance of EMA is tested on standard IEEE test systems.The selection of control parameters of EMA is done through exhaustive parametric study. In this paper, an exchange market algorithm (EMA) approach is applied to solve highly non-linear power system optimal reactive power dispatch (ORPD) problems. ORPD is most vital optimization problems in power system study and are usually devised as optimal power flow (OPF) problem. The problem is formulated as nonlinear, non-convex constrained optimization problem with the presence of both continuous and discrete control variables. The EMA searches for optimal solution via two main phases; namely, balanced market and oscillation market. Each of the phases comprises of both exploration and exploitation, which makes the algorithm unique. This uniqueness of EMA is exploited in this paper to solve various vital objectives associated with ORPD problems. Programs are developed in MATLAB and tested on standard IEEE 30 and IEEE 118 bus systems. The results obtained using EMA are compared with other contemporary methods in the literature. Simulation results demonstrate the superiority of EMA in terms of its computational efficiency and robustness. Consumed function evaluation for each case study is mentioned in the convergence plot itself for better clarity. Parametric study is also performed on different case studies to obtain the suitable values of tuneable parameters.

This paper presents an approach for breast cancer diagnosis in digital mammograms using wave atom transform.I examine the wave atom transform to determine which couple (scale, ratio of biggest coefficients) will give the highest classification rate.The system uses two sets of feature matrixes obtained from two different database; MIAS and DDSM database.These are tested with classifiers in changing ratios (10% and 90% of coefficients) for each scale.The classification is performed using two different classifiers; Support Vector Machine and k-Nearest Neighbors. This paper presents an approach for breast cancer diagnosis in digital mammograms using wave atom transform. Wave atom is a recent member of the multi-resolution representation methods. Primarily, the mammogram images are decomposed on the basis of wave atoms, and then a special set of the biggest coefficients from wave atom transform is used as a feature vector. Two different classifiers, support vector machine and k-nearest neighbors, are employed to classify mammograms. The method is tested using two different sets of images provided by MIAS and DDSM database.

This paper introduces weight uncertainty into ELM-AE and presents WuELM-AE.WuELM-AE extracts useful features that constitute higher level representation.ML-WuELM stacks WuELM-AE to create a multi layer network.Proposed ML-ELM-RBF achieves satisfactory results on multi-label data sets. Many neural network methods such as ML-RBF and BP-MLL have been used for multi-label classification. Recently, extreme learning machine (ELM) is used as the basic elements to handle multi-label classification problem because of its fast training time. Extreme learning machine based auto encoder (ELM-AE) is a novel method of neural network which can reproduce the input signal as well as auto encoder, but it can not solve the over-fitting problem in neural networks elegantly. Introducing weight uncertainty into ELM-AE, we can treat the input weights as random variables following Gaussian distribution and propose weight uncertainty ELM-AE (WuELM-AE). In this paper, a neural network named multi layer ELM-RBF for multi-label learning (ML-ELM-RBF) is proposed. It is derived from radial basis function for multi-label learning (ML-RBF) and WuELM-AE. ML-ELM-RBF firstly stacks WuELM-AE to create a deep network, and then it conducts clustering analysis on samples features of each possible class to compose the last hidden layer. ML-ELM-RBF has achieved satisfactory results on single-label and multi-label data sets. Experimental results show that WuELM-AE and ML-ELM-RBF are effective learning algorithms.

The paper presents techniques for autonomous path tracking of heavy vehicles at high speeds.Vehicle state estimation is based on fuzzy logic, and a Takagi-Sugeno type controller is used for vehicle lateral control.The paper describes the real implementation and testing of the controllers in a real truck.The implementation of high speed robot controllers for heavy vehicles is more difficult in practice than for small mobile robots.The paper also describes experiments with the soft controller at high speeds, up to 100km/h. This paper presents techniques for GPS based autonomous navigation of heavy vehicles at high speed. The control system has two main functions: vehicle position estimation and generation of the steering commands for the vehicle to follow a given path autonomously. Position estimation is based on fusion of measurements from a carrier-phase differential GPS system and odometric sensors using fuzzy logic. A Takagi-Sugeno fuzzy controller is used for steering commands generation, to cope with different road geometry and vehicle velocity. The presented system has been implemented in a 13tons truck, and fully tested in very demanding conditions, i.e. high velocity and large curvature variations in paved and unpaved roads.

This paper proposes a two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm for the prize-collecting vehicle routing problem (PCVRP) based on the fact there are two levels of decisions in the PCVRP, namely the selection of customers to visit and the vehicle scheduling of selected customers. Computational results illustrates the efficiency of the proposed algorithm. A two-level self-adaptive variable neighborhood search algorithm is developed based on the two levels of decisions in the PCVRP, namely the selection of customers and the vehicle scheduling of selected customers.The neighborhood sequence can be self-adaptively changed based on the analysis of the search history.A graph extension method is adopted to obtain the lower bound for PCVRP by transforming the proposed mixed integer programming model of PCVRP into an equivalent traveling salesman problem model. This paper investigates the prize-collecting vehicle routing problem (PCVRP), which has a strong background in practical industries. In the PCVRP, the capacities of all available vehicles are not sufficient to satisfy the demands of all customers. Consequently it is not a compulsory requirement that all customers should be visited. However, a prize can be collected once a customer is visited. In addition, it is required that the total demands of visited customers should reach a pre-specified value at least. The objective is to establish a schedule of vehicle routes so as to minimize the total transportation cost and at the same time maximize the prize collected by all vehicles. The total transportation cost consists of the total distance of vehicle routes and the sum of vehicles used in the schedule. To solve the PCVRP, a two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm is developed according to the two levels of decisions in the PCVRP, namely the selection of customers to visit and the visiting sequence of selected customers in each vehicle route. The proposed TLSAVNS algorithm is self-adaptive because the neighborhoods and their search sequence are determined automatically by the algorithm itself based on the analysis of its search history. In addition, a graph extension method is adopted to obtain the lower bound for PCVRP by transforming the proposed mixed integer programming model of PCVRP into an equivalent traveling salesman problem (TSP) model, and the obtained lower bound is used to evaluate the proposed TLSAVNS algorithm. Computational results on benchmark problems show that the proposed TLSAVNS algorithm is efficient for PCVRP.

We propose a new subband implementation of the Forward BSS algorithm.We proposed a new two-channel variable step-size techniques adapted to the proposed FBSS algorithm.The new FBSS algorithm is used to separate speech form noise and therefore enhance the speech signal intelligibility.The new algorithm improves the steady state values of the cross adaptive filters which are used by the FBSS algorithm. This paper addresses the problem of speech enhancement and acoustic noise reduction by adaptive filtering algorithms. Recently, we have proposed a new Forward blind source separation algorithm that enhances very noisy speech signals with a subband approach. In this paper, we propose a new variable subband step-sizes algorithm that allows improving the previous algorithm behaviour when the number of subband is selected high. This new proposed algorithm is based on recursive formulas to compute the new variable step-sizes of the cross-coupling filters by using the decorrelation criterion between the estimated sub-signals at each subband output. This new algorithm has shown an important improvement in the steady state and the mean square error values. Along this paper, we present the obtained simulation results by the proposed algorithm that confirm its superiority in comparison with its original version that employs fixed step-sizes of the cross-coupling adaptive filters and with another fullband algorithm.

Graphical abstractDisplay Omitted HighlightsThe paper presents an improvement of control performance of sliding mode control.The proposed method reduces duration of the undesirable approaching phase.The control parameters are tuned by fuzzy logic rules.The performance improvement was confirmed by simulation study. A continuous sliding mode control with moving sliding surface for nonlinear systems of arbitrary order is presented in this paper. The sliding surface is moved repetitively toward the target sliding surface in order to ensure that the system trajectory is close to the actual surface during the whole control process. The parameters of sliding mode control are tuned by a fuzzy logic. The proposed procedure reduces the time when the system operates in the approaching phase during which the control performance is deteriorated since the system is more susceptible to external disturbances and model uncertainties. The effectiveness of the presented approach is demonstrated on a control of a flexible robot manipulator arm.

Developing an IVIF permutation method with likelihood-based preference functions.Using a hybrid approach of the likelihood calculation method and PROMETHEE.Establishing new measurements of concordance/discordance to evaluate permutations.Presenting two algorithms for IVIF importance weights and incomplete information.Verifying the effectiveness using comparative analyses and applications to NPD. This paper presents an interval-valued intuitionistic fuzzy permutation method with likelihood-based preference functions for managing multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets. First, certain likelihood-based preference functions are proposed using the likelihoods of interval-valued intuitionistic fuzzy preference relationships. Next, selected practical indices of concordance/discordance are established to evaluate all possible permutations of the alternatives. The optimal priority order of the alternatives is determined by comparing all comprehensive concordance/discordance values based on score functions. Furthermore, this paper considers various preference types and develops another interval-valued intuitionistic fuzzy permutation method using programming models to address multiple criteria decision-making problems with incomplete preference information. The feasibility and applicability of the proposed methods are illustrated in the problem of selecting a suitable bridge construction method. Moreover, certain comparative analyses are conducted to verify the advantages of the proposed methods compared with those of other decision-making methods. Finally, the practical effectiveness of the proposed methods is validated with a risk assessment problem in new product development.

This paper proposes a hybrid time-series ANFIS model based on EMD to forecast stock price.In order to evaluate the forecasting performances, the proposed model is compared with other models.The experimental results show that proposed model is superior to the listing models. Time series forecasting is an important and widely popular topic in the research of system modeling, and stock index forecasting is an important issue in time series forecasting. Accurate stock price forecasting is a challenging task in predicting financial time series. Time series methods have been applied successfully to forecasting models in many domains, including the stock market. Unfortunately, there are 3 major drawbacks of using time series methods for the stock market: (1) some models can not be applied to datasets that do not follow statistical assumptions; (2) most time series models that use stock data with a significant amount of noise involutedly (caused by changes in market conditions and environments) have worse forecasting performance; and (3) the rules that are mined from artificial neural networks (ANNs) are not easily understandable.To address these problems and improve the forecasting performance of time series models, this paper proposes a hybrid time series adaptive network-based fuzzy inference system (ANFIS) model that is centered around empirical mode decomposition (EMD) to forecast stock prices in the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and Hang Seng Stock Index (HSI). To measure its forecasting performance, the proposed model is compared with Chen's model, Yu's model, the autoregressive (AR) model, the ANFIS model, and the support vector regression (SVR) model. The results show that our model is superior to the other models, based on root mean squared error (RMSE) values.

Define a ratio-based similarity index for a pair of interval-valued cross ratios.Develop new similarity measures for interval reciprocal preference relations (IRPRs).Devise an induced interval-valued cross ratio ordered weighted geometric operator.Develop a consensus model to solve group decision making problems with IRPRs. Similarity analysis and preference information aggregation are two important issues for consensus building in group decision making with preference relations. Pairwise ratings in an interval reciprocal preference relation (IRPR) are usually regarded as interval-valued And-like representable cross ratios (i.e., interval-valued cross ratios for short) from the multiplicative perspective. In this paper, a ratio-based formula is introduced to measure similarity between a pair of interval-valued cross ratios, and its desirable properties are provided. We put forward ratio-based similarity measurements for IRPRs. An induced interval-valued cross ratio ordered weighted geometric (IIVCROWG) operator with interval additive reciprocity is developed to aggregate interval-valued cross ratio information, and some properties of the IIVCROWG operator are presented. The paper devises an importance degree induced IRPR ordered weighted geometric operator to fuse individual IRPRs into a group IRPR, and discusses the derivation of its associated weights. By employing ratio-based similarity measurements and IIVCROWG-based aggregation operators, a soft consensus model including a generation mechanism of feedback recommendation rules is further proposed to solve group decision making problems with IRPRs. Three numerical examples are examined to illustrate the applicability and effectiveness of the developed models.

This paper first analyzes the size of the interval priority weight.Two models are built to derive the interval priority weight of each object.Then, two models are built to cope with the inconsistency for each object.Furthermore, two models are constructed to derive the missing values.Numerical examples and comparison analysis are made. Interval fuzzy preference relations that can well cope with the vagueness and uncertainty are commonly used by the decision maker. The most crucial issue is how to derive the interval priority vector from an interval fuzzy preference relation. This paper first analyzes the size of the interval priority weights. Then, two linear programming models are built, by which the interval priority weights are obtained, respectively. Considering the inconsistent case, two consistency-based linear programming models are built to derive the additive consistent fuzzy preference relations. Different to the current methods, new models consider the consistency and the interval priority weight simultaneously. In some situations, the decision maker may only offer an incomplete interval fuzzy preference relation, namely, some judgments are missing. To cope with this situation, we first classify the missing intervals into three categories and then apply the associated linear equations to denote the missing values. After that, we construct two consistency-based linear programming models to determine the missing values to cope with the consistent and inconsistent cases. It is worth noting that the built models can cope with the situation where ignorance objects exist. Meanwhile, the associated numerical examples are offered, and the analysis comparison is made.

This paper presents a new optimization approach for data clustering with COA.High quality results obtained for dataset.This paper presents a new optimization approach for data clustering with COA and fuzzy system for clustering data.An efficient proposal for data clustering by Cuckoo Optimization Algorithm.In this proposal at each iteration, firstly generates r cuckoo's agents. Each cuckoo generates a random solution string and tries to calculate a fitness value for its solution. Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.

Graphical abstractDisplay Omitted HighlightsWe constructed a Linear Deceptive function as the representative of a class of multimodal functions.DE cannot guarantee convergence in probability on the above class of multimodal functions.A random drift model was firstly used to analyze the convergence of a real-coded evolutionary algorithm.DE's mutation operators prefer to search in the aggregating region of the target individuals. The theoretical studies of differential evolution algorithm (DE) have gradually attracted the attention of more and more researchers. According to recent researches, the classical DE cannot guarantee global convergence in probability except for some special functions. Along this perspective, a problem aroused is that on which functions DE cannot guarantee global convergence. This paper firstly addresses that DE variants are difficult on solving a class of multimodal functions (such as the Shifted Rotated Ackley's function) identified by two characteristics. One is that the global optimum of the function is near a boundary of the search space. The other is that the function has a larger deceptive optima set in the search space. By simplifying the class of multimodal functions, this paper then constructs a Linear Deceptive function. Finally, this paper develops a random drift model of the classical DE algorithm to prove that the algorithm cannot guarantee global convergence on the class of functions identified by the two above characteristics.

Saturation nonlinearity effect on the ANC performance is addressed.PSO based nonlinear structure ANC algorithm is proposed.Filter bank based FLANN structure is tuned with PSO.The proposed ANC algorithm does not require secondary path estimate.Computer simulations are carried out for performance evaluation.Sensitivity analysis is done for PSO parameter tuning. In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.

Graphical abstractDisplay Omitted HighlightsLipschitz aggregation property and copula characteristic of t-norms and implications are discussed and the robustness of rule-based fuzzy reasoning is investigated.According to Lipschitz aggregation property and copula characteristic of t-norms and implications, suitable t-norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning.The approach provides guidance for choosing suitable fuzzy connective operators and decision making application in rule-based fuzzy reasoning.The experiments which illustrate the ideas are the applications of intelligent information processing. The analysis of internal connective operators of fuzzy reasoning is very significant and the robustness of fuzzy reasoning has been calling for study. An interesting and important question is that, how to choose suitable internal connective operators to guarantee good robustness of rule-based fuzzy reasoning? This paper is intended to answer it. In this paper, Lipschitz aggregation property and copula characteristic of t-norms and implications are discussed. The robustness of rule-based fuzzy reasoning is investigated and the relationships among input perturbation, rule perturbation and output perturbation are presented. The suitable t-norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning. In 1-Lipschitz operators, if both t-norm and implication are copulas, the rule-based fuzzy reasoning is much more stable and more reliable. In copulas, if both t-norm and implication are 1-l∞-Lipschitz, they can guarantee good robustness of fuzzy reasoning. The experiments not only illustrate the ideas proposed in the paper but also can be regarded as applications of soft computing. The approach in the paper also provides guidance for choosing suitable fuzzy connective operators and decision making application in rule-based fuzzy reasoning.

We developed a new fuzzy mixed-integer linear programming model for SALBP and SULBP.We proposed a new two-phase interactive fuzzy programming to solve the problems.Our interactive fuzzy approach is capable to handle any practical MOLP models. The consideration of this paper is given to address the straight and U-shaped assembly line balancing problem. Although many attempts in the literature have been made to develop deterministic version of the assembly line model, the attention is not considerably given to those in uncertain environment. In this paper, a novel bi-objective fuzzy mixed-integer linear programming model (BOFMILP) is developed so that triangular fuzzy numbers (TFNs) are employed in order to represent uncertainty and vagueness associated with the task processing times in the real production systems. In this proposed model, two conflicting objectives (minimizing the number of stations as well as cycle time) are considered simultaneously with respect to set of constraints. For this purpose, an appropriate strategy in which new two-phase interactive fuzzy programming approach is proposed as a solution method to find an efficient compromise solution. Finally, validity of the proposed model as well as its solution approach are evaluated though numerical examples. In addition, a comparison study is conducted over some test problems in order to assess the performance of the proposed solution approach. The results demonstrate that our proposed interactive fuzzy approach not only can be applied in ALBPs but also is capable to handle any practical MOLP models. Moreover, in light of these results, the proposed model may constitute a framework aiming to assist the decision maker (DM) to deal with uncertainty in assembly line problem.

HighlightsSystem, which operates on the basis of dispersed knowledge is considered.New approach to the organization of the system's structure was proposed.The local knowledge bases will be combined into groups (coalitions) in a dynamic way.The elimination inconsistencies in the knowledge will be implemented in the groups.Global decisions will be made by using one of the methods for analysis of conflicts. This paper discusses the issues related to the process of global decision-making on the basis of knowledge which is stored in several local knowledge bases. The approach considered in this paper is very general because we do not assume any additional conditions on the sets of objects or the sets of conditional attributes of local knowledge bases.The paper proposes a new approach to the organization of the structure of multi-agent decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the local knowledge bases will be combined into groups in a dynamic way. We will seek to designate groups of local bases on which the test object is classified to the decision classes in a similar manner. Then, a process of the elimination inconsistencies in the knowledge will be implemented in the created groups. Global decisions will be made by using one of the methods for analysis of conflicts.The paper includes the definition of a multi-agent decision-making system with dynamically generated clusters and a description of a global decision-making process. In addition, the paper presents the results of experiments carried out on data from the UCI repository.

A robust neural-network-method applied to speed estimation in line-connected three-phase induction motors is presented.A comparative study of single and multiple current sensor applied to speed estimation is presented.Proposed method is validated using simulation and experimental results considering unbalance operation conditions and wide range of load torque.Proposed method is embedded in a digital processor and validated in a test bench. Estimating the electrical and mechanical parameters involved in three-phase induction motors is frequently employed to avoid measuring every variable in the process. Among mechanical parameters, speed is an important variable: it is involved in control, diagnosis, condition monitoring, and can be measured or estimated by sensorless methods. These technologies offer advantages when compared with direct measurement, such as lower cost or more robust systems. This paper proposes the use of artificial neural networks to estimate rotor speed by using current sensors for balanced and unbalanced voltage sources with a wide mechanical load range in a line-connected induction motor. This paper also presents two case analyses: (i) a single current sensor; and (ii) a multiple currents sensors. Simulation and experimental results are presented to validate the proposed approach. A neural speed estimator embedded in a digital processor is also presented.

Graphical abstractDisplay Omitted HighlightsNovel fuzzy rule-based classifiers (FRBCs) for financial data classification tasks are proposed.FRBCs are designed using multi-objective evolutionary optimization algorithms.Collection of FRBCs with various levels of accuracy-interpretability trade-off is generated.Benchmark financial data sets are used to evaluate the effectiveness of the proposed system.Highly accurate, interpretable and fast financial decision support is provided by our approach. Credit classification is an important component of critical financial decision making tasks such as credit scoring and bankruptcy prediction. Credit classification methods are usually evaluated in terms of their accuracy, interpretability, and computational efficiency. In this paper, we propose an approach for automatic designing of fuzzy rule-based classifiers (FRBCs) from financial data using multi-objective evolutionary optimization algorithms (MOEOAs). Our method generates, in a single experiment, an optimized collection of solutions (financial FRBCs) characterized by various levels of accuracy-interpretability trade-off. In our approach we address the complexity- and semantics-related interpretability issues, we introduce original genetic operators for the classifier's rule base processing, and we implement our ideas in the context of Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the presently most advanced MOEOAs. A significant part of the paper is devoted to an extensive comparative analysis of our approach and 24 alternative methods applied to three standard financial benchmark data sets, i.e., Statlog (Australian Credit Approval), Statlog (German Credit Approval), and Credit Approval (also referred to as Japanese Credit) sets available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml). Several performance measures including accuracy, sensitivity, specificity, and some number of interpretability measures are employed in order to evaluate the obtained systems. Our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained financial data classifiers while remaining either competitive or superior in terms of their accuracy and the speed of decision making.

Numerical computations were conducted under many conditions, including different chromosome lengths and search sizes.The good experimental performance of two schemes indicates that the genetic search proposed in this paper is a better way to locate exactly a constrained point at geometric discontinuity.Our algorithm is invariant of chromosome length and search space size to a certain degree. Advanced sensing technologies have produced a significant amount of discrete point data in the past decade. Measurement uncertainty frequently occurs at the geometric discontinuity of mechanical parts. In this paper, a genetic search algorithm is developed for optimally-constrained multiple-line fitting of discrete data points. It contains two important technical components: (a) constrained least-squares fitting of multiple lines, and (b) genetic search for optimal corner/edge points. The algorithm is designed for both two-dimensional and three-dimensional cases. Numerical experiments demonstrate the effectiveness of the proposed approach, compared to the conventional least-squares fitting method as well as exhaustive search method. A comparative study with a particle swarm method indicates that both the genetic search and particle swarm search produce similar results in terms of minimum fitting errors. It can be used for the effective determination of sharp edges or corners based on discrete data points measured for high-precision industrial inspection and manufacturing.

Three dimensional integrated circuits (3D ICs) can alleviate the problem of interconnection, a critical problem in the nanoscale era, and are also promising for heterogeneous integration. This paper proposes a two-phase method combining the ant system algorithm (AS) and simulated annealing (SA) to handle 3D IC floorplanning with fixed-outline constraints. In the first AS phase, the floorplans are constructed by sequentially packing the block one by one, and the AS is used to explore the appropriate packing order and device layer assignment for the blocks. When packing a block, a proper position including the coordinates and the appropriate layer in the partially constructed floorplan should be chosen from all possible positions. While packing the blocks, a probability layer assignment strategy is proposed to determine the device layer assignment of unpacked blocks. After the AS phase, the SA phase is used to perform further optimization. The proposed method can also be easily applied to 2D floorplanning problems. Compared with the state of the art 3D/2D fixed-outline floorplanner, the experimental results demonstrate the effectiveness of the proposed method. The ant system algorithm and simulated annealing were combined to solve 3D/2D fixed-outline floorplanning problem.In the AS phase, a floorplan construction method is proposed, in which the blocks are greedily packed into the chip area one by one.During block packing, a probability layer assignment strategy is proposed to determine the layer assignment of the unpacked blocks.We define the pheromone in AS using the packing order of block b, and the pheromone update rule also adapts to handle the 3D floorplanning problem.We combine the area of a block with the connection degree (CD) between blocks as the heuristic information. Three dimensional integrated circuits (3D ICs) can alleviate the problem of interconnection, a critical problem in the nanoscale era, and are also promising for heterogeneous integration. This paper proposes a two-phase method combining the ant system algorithm (AS) and simulated annealing (SA) to handle 3D IC floorplanning with fixed-outline constraints. In the first AS phase, the floorplans are constructed by sequentially packing the block one by one, and the AS is used to explore the appropriate packing order and device layer assignment for the blocks. When packing a block, a proper position including the coordinates and the appropriate layer in the partially constructed floorplan should be chosen from all possible positions. While packing the blocks, a probability layer assignment strategy is proposed to determine the device layer assignment of unpacked blocks. After the AS phase, the SA phase is used to perform further optimization. The proposed method can also be easily applied to 2D floorplanning problems. Compared with the state of the art 3D/2D fixed-outline floorplanner, the experimental results demonstrate the effectiveness of the proposed method.

Economic/emission dispatch problem (EED) is solved.Transmission network loss and valve-point effects are considered.Multi-objective backtracking search algorithm (MOBSA) is developed.Weighted sum method (WSM) and non-dominated approach (NDA) are employed.The proposed method is able to solve EED problems efficiently. This paper presents the application of backtracking search algorithm (BSA) for solving an economic/emission dispatch (EED) problem as a multi-objective optimization problem. BSA is a newly developed evolutionary algorithm with one control parameter to solve numerical optimization problems. It utilizes crossover and mutation operators to advance optimization toward the optimal. The multi-objective BSA developed and presented in this paper uses an elitist external archive to store non-dominated solutions known as pareto front. The problem of EED is also solved by weighted sum method, which combines both objectives of the problem into a single objective. Three test systems are the case studies verifying the effectiveness of BSA. The results are compared with those of other methods in literatures and confirm the high performance of BSA.

Graphical abstractDisplay Omitted HighlightsConstrained particle swarm optimization algorithm is used to tune the parameters of a robust PID controller.The design is oriented towards robust control performance and robust stability.Various control-performance criteria can be easily implemented.Robust stability within the intervals of the uncertain parameters is ensured.Useful, computationally tractable and efficient framework based on interval models. PID controller structure is regarded as a standard in the control-engineering community and is supported by a vast range of automation hardware. Therefore, PID controllers are widely used in industrial practice. However, the problem of tuning the controller parameters has to be tackled by the control engineer and this is often not dealt with in an optimal way, resulting in poor control performance and even compromised safety. The paper proposes a framework, which involves using an interval model for describing the uncertain or variable dynamics of the process. The framework employs a particle swarm optimization algorithm for obtaining the best performing PID controller with regard to several possible criteria, but at the same time taking into account the complementary sensitivity function constraints, which ensure robustness within the bounds of the uncertain parameters' intervals. Hence, the presented approach enables a simple, computationally tractable and efficient constrained optimization solution for tuning the parameters of the controller, while considering the eventual gain, pole, zero and time-delay uncertainties defined using an interval model of the controlled process. The results provide good control performance while assuring stability within the prescribed uncertainty constraints. Furthermore, the controller performance is adequate only if the relative system perturbations are considered, as proposed in the paper. The proposed approach has been tested on various examples. The results suggest that it is a useful framework for obtaining adequate controller parameters, which ensure robust stability and favorable control performance of the closed-loop, even when considerable process uncertainties are expected.

This paper presents KH algorithm to solve optimal placement of distributed generator (ODG) problem.ODG problem is studied with an objective of reducing power loss and energy cost.Three illustrative examples of radial distribution network are presented.Proposed method shows better results when compared with other techniques in terms of the quality of solution. Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.

Graphical abstractDisplay Omitted HighlightsWe consider a constrained three-objective optimization portfolio selection problem.We solve the problem by means of evolutionary multi-objective optimization.New mutation, crossover and reparation operators are designed for this problem.They are tested in several algorithms for a data set from the Spanish stock market.Results for two performance metrics reveal the effectiveness of the new operators. In this paper, we consider a recently proposed model for portfolio selection, called Mean-Downside Risk-Skewness (MDRS) model. This modelling approach takes into account both the multidimensional nature of the portfolio selection problem and the requirements imposed by the investor. Concretely, it optimizes the expected return, the downside-risk and the skewness of a given portfolio, taking into account budget, bound and cardinality constraints. The quantification of the uncertain future return on a given portfolio is approximated by means of LR-fuzzy numbers, while the moments of its return are evaluated using possibility theory. The main purpose of this paper is to solve the MDRS portfolio selection model as a whole constrained three-objective optimization problem, what has not been done before, in order to analyse the efficient portfolios which optimize the three criteria simultaneously. For this aim, we propose new mutation, crossover and reparation operators for evolutionary multi-objective optimization, which have been specially designed for generating feasible solutions of the cardinality constrained MDRS problem. We incorporate the operators suggested into the evolutionary algorithms NSGAII, MOEA/D and GWASF-GA and we analyse their performances for a data set from the Spanish stock market. The potential of our operators is shown in comparison to other commonly used genetic operators and some conclusions are highlighted from the analysis of the trade-offs among the three criteria.

Graphical abstractDisplay Omitted HighlightsOur algorithm owns good adaptability to deal with some practical problems.Our algorithm obtains better or comparable performance than other algorithms.The solutions are helpful for hybrid between neural nets and evolutionary algorithms. This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH-EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.

A systematic method combined subjective and objective indicators is proposed.An index system involving the process of knowledge management is established.A synthetic evaluation method using triangular fuzzy number is presented.This evaluation method has strong practicability and operability. The purpose of this paper is to propose a systematic method to solve knowledge management performance evaluation (KMPE) problems. This method includes an integrated evaluation process starting from the measurement to the output of KMPE and combines subjective and objective indicators together. Firstly, we established an index system, involving the process of knowledge management, the organizational knowledge structure, economic benefits and efficiency. And based on this index system, a synthetic evaluation method is presented, using triangular fuzzy number to measure indexes and facilitating the KMPE with a group support system (GSS). To know better of the proposed method, we have an example to illustrate. Finally, the empirical study conducted in this paper indicates that the evaluation method has strong practicability and operability. Besides, the evaluation is enabled by using a group support system: the more objective scoring can be achieved due to synchronic/asynchronous and anonymous participation; Decision-makers improve their efficiency by the clear demonstration analysis results. The systematic method of KMPE based on the index system is able to improve organizations' efficiency in performance evaluation process.

Decision hierarchy for software selection process has four phases, which includes goal, criteria, sub-criteria and alternatives. This paper ensures an analytical tool to define the most convenient software.Fuzzy AHP determines the weights for criteria of software selection problem.Fuzzy TOPSIS defines the most appropriate alternative in uncertain environment.It decreases the uncertainty and the information loss in group decision making.Further study can focus on interval type 2 fuzzy set to develop a decision support system. This paper aims to ease group decision-making by using an integration of fuzzy AHP (analytic hierarchy process) and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) and its application to software selection of an electronic firm. Firstly, priority values of criteria in software selection problem have been determined by using fuzzy extension of AHP method. Fuzzy extension of AHP is suggested in this paper because of little computation time and much simpler than other fuzzy AHP procedures. Then, the result of the fuzzy TOPSIS model can be employed to define the most appropriate alternative with regard to this firm's goals in uncertain environment. Fuzzy numbers are presented in all phases in order to overcome any vagueness in decision making process. The final decision depends on the degree of importance of each decision maker so that wrong degree of importance causes the mistaken result. The researchers generally determine the degrees of importance of each decision maker according to special characteristics of each decision maker as subjectivity. In order to overcome this subjectivity in this paper, the judgments of decision makers are degraded to unique decision by using an attribute based aggregation technique. There is no study about software selection using integrated fuzzy AHP-fuzzy TOPSIS approach with group decision-making based on an attribute based aggregation technique. The results of the proposed approach and the other approaches are compared. Results indicate that our methodology allows decreasing the uncertainty and the information loss in group decision making and thus, ensures a robust solution to the firm.