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Many clustering algorithms categorized as K-clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K-clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K-Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GAs) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm.

It is a very challenging and important task to adaptively adjust the scale factor F and the crossover rate Cr for Differential Evolutionary (DE) algorithms. Most recent adaptive techniques were designed to generate parameters randomly based on successful trial values during the previous evolving process, lacking explicit guidelines to generate appropriate values. This paper proposes a novel parameter adaption strategy, which could incorporate promising F and Cr pairs extracted by using Association Rule Mining (ARM) into DE algorithms. First, all successful F and Cr values generated by their original methods are recorded during the whole evolution, resulting in an increasing dataset. Second, we discretize the dataset and extract the most frequent itemset of parameters by using a modified version of the widely used Apriori algorithm. Third, a greedy operator is developed to generate new parameters in the next generation by comparing the presented ARM-based and original-method-based fitness values. The presented technique provides an additional pair of F and Cr values to be evaluated, without replacing existing strategies for the control parameters. The main contribution of this paper is that we propose a novel way, which utilizes information generated during the evolutionary process, to enhance exploration capabilities by adjusting control parameters. Experimental results demonstrate that the proposed ARM-based parameter adaptive strategy is able to enhance performances of some state-of-the-art DE variants. Further, this methodology might be helpful for other control parameters of Evolutionary Algorithms (EA).

Activation functions play a crucial role in discriminative capabilities of the deep neural networks. They are also one of the main reasons for revival of the neural networks. Although, recent activation functions provide solution to vanishing and exploding gradient problems, but they do not have sufficient capacity to model non-linear data. This paper proposes a novel activation function, which imparts neural networks with capability to model non-linear dependencies in data. Behavior of proposed activation function remains non-saturating even with non-linear structure. The activation function provides distinct activation behaviors to different data range segments. The paper presents two different variants of proposed function. The first, “linear sigmoidal activation” function is a fixed structure activation function with function coefficients defined at the start of model design. Whereas second, “adaptive linear sigmoidal activation” function is a trainable function which can adapt itself according to the complexity of the given data. Both of the proposed models are tested against the state of the art activation functions on benchmark datasets (CIFAR-10, MNIST, SVHN, FER-2013). The proposed activation function is able to outperforms every known activation functions in all the tests.

This paper introduces a new method for calculating distances between matrix sequences of different length. Matrix sequences are often used in spatiotemporal analysis, a field of research combining topological frameworks and temporal reasoning. The Qualitative Trajectory Calculus, a spatiotemporal calculus using matrix sequences, is used as an example in this paper. Since current approaches for comparing sequences are not suitable to calculate distances between matrix sequences of different length, a new method is proposed. The method is based on sequence alignment and uses the Levenshtein distance metric as starting point. The method is presented in general at first, followed by an application to QTC-matrix sequences and a three cushion billiards movement pattern detection example. Possible applications in expert and intelligent systems are discussed, such as human-robot interaction with multiple robots, and automated traffic analysis of (autonomous) vehicle interactions. The proposed method, however, can be applied in a broad range of domains, whenever distances need to be calculated between matrix sequences of different length.

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods.

A model of the unequal-area facility layout problem is described.A modified multi-objective particle swarm optimization algorithm is proposed.We apply the heuristic strategy to update layout.The gradient method is applied to execute local search.The objective space division method is used to find the Pbest and Gbest. The facility layout problem (FLP) is the problem of placing facilities in a certain shop floor so that facilities do not overlap each other and are satisfied with some given objectives. Considering practical situations, this study focuses on the multi-objective unequal-area facility layout problem (UA-FLP), where the facilities have unequal-areas and fixed shapes and are placed orthogonally in the continuous shop floor. The objectives of the problem aim to optimize the material handling cost, the total adjacency value and the utilization ratio of the shop floor. The chief difficulties of this version of the FLP lie in the satisfaction of non-overlapping constraint between any two different facilities and the optimization of multiple objectives in the huge solution space. In this paper, we put forward a heuristic configuration mutation operation and subsequent local search based on the gradient method to satisfy the non-overlapping constraint, and the multi-objective particle swarm optimization (MOPSO) algorithm, which has recently proven its high effectiveness and robustness in solving multi-objective problems, to obtain a set of Pareto-optimal solutions of the problem. The novelty of the paper lies in the use of an objective space division method in the MOPSO which governs the neighborhood topology and the local best swarm used to assess the global fitness of a solution and choose the global leader particle. The proposed algorithm is tested on three sets of different UA-FLPs from the literature with the size of the problem up to 62 facilities. The numerical results show that the proposed method is effective in solving the multi-objective UA-FLP.

Mining incrementally closed item sets is an essential problem in data mining. Once closed item sets and their frequencies are found, the frequentation of a found closed item set is always determined. In recent times, concept lattice has been used as an intermediate structure for the purpose of mining incrementally (frequent) closed sets. However, lattice operations are very time-consuming when data fluctuates as well as the memory for lattice is consumed a lot when data becomes large. This paper proposes an intermediate structure called constructive set to produce closed sets along with their occurrence frequencies. The constructive set is constructed from a set of group patterns – an extended form of bit chains. This paper also proposes algorithms based on the constructive set for mining incrementally closed sets when adding and removing transactions. The proposed algorithms along with storing and calculating on bit data have significant advantages shown through experiments and comparisons.

Quantum chaotic cuckoo search algorithm is proposed for the data clustering problem.The performance of the proposed approach was assessed on six well known datasets.The Chaos maps and Boundary handling strategy enhance the cuckoo search algorithm.The nonhomogeneous quantum update improves the global search ability.The significant superiority of the proposed algorithm over eight recent algorithms. This paper presents a new quantum chaotic cuckoo search algorithm (QCCS) for data clustering. Recent researches show the superiority of cuckoo search (CS) over traditional meta-heuristic algorithms for clustering problems. Unfortunately, all the cuckoos have identical search behaviours that may lead the algorithm to converge to local optima. Also, the convergence rate is sensitive to initial centroids seeds that are randomly generated.Therefore, the main contribution of this paper is to extend the CS capabilities using nonhomogeneous update inspired by the quantum theory in order to tackle the cuckoo search clustering problem in terms of global search ability. Also, the randomness at the beginning step is replaced by the chaotic map in order to make the search procedure more efficient and improve the convergence speed. In addition, an effective strategy is developed to well manage the boundaries.The experimental results on six famous real-life datasets show the significant superiority of the proposed QCCS over eight recent well known algorithms including, genetic quantum cuckoo search, hybrid cuckoo search and differential evolution, hybrid K-means and improved cuckoo search, standard cuckoo search, quantum particle swarm optimization, differential evolution, hybrid K-means chaotic particle swarm optimization and genetic algorithm for all benchmark datasets in terms of internal and external clustering quality.

Process monitoring has been widely recognized as an important and critical tool in system monitoring for detection of abnormal behavior and quality improvement. In manufacturing processes or industrial systems, several sources of out-of-control variation, such as tool wear, gradual equipment deterioration, vibration, inconsistent material etc., often result in dynamic changes in the process parameter, or result in special residual signals of the systems. Detecting such weak or decaying signals is a challenging task. Although the window-limited generalized likelihood ratio (wl-GLR) scheme is widely used in changepoint detection and has some advantages, it may perform poorly when it is used to detect weak or decaying signals. This paper proposes a new change-detection scheme, the window-limited weighted likelihood ratio (wl-WLR) scheme, to improve the wl-GLR scheme. To do this, a new statistical distance measure called the GLR divergence is first defined and then its properties are analyzed. The wl-WLR scheme is designed to monitor the weighted average of the GLR divergences in a moving window, and the wl-GLR scheme can be viewed as a special case of the wl-WLR scheme. Two types of weight functionals are introduced and investigated for the wl-WLR scheme. Numerical algorithms to select the optimal weight parameters are provided based on a calibration sample. Extensive simulation study favors the proposed wl-WLR scheme for detecting weak or decaying signals, and its performance is robust even if the weight parameters are not accurately estimated. This paper has online supplementary materials.

Given the higher demands in network bandwidth and speed that the Internet will have to meet in the near future, it is crucial to research and design intelligent and proactive congestion control and avoidance mechanisms able to anticipate decisions before the congestion problems appear. Nowadays, congestion control mechanisms in the Internet are based on TCP, a transport protocol that is totally reactive and cannot adapt to network variability because its convergence speed to the optimal values is slow. In this context, we propose to investigate new congestion control mechanisms that (a) explicitly compute the optimal sessions' sending rates independently of congestion signals (i.e., proactive mechanisms) and (b) take anticipatory decisions (e.g., using forecasting or prediction techniques) in order to avoid the appearance of congestion problems. In this paper we present B-Neck, a distributed optimization algorithm that can be used as the basic building block for the new generation of proactive and anticipatory congestion control protocols. B-Neck computes proactively the optimal sessions' sending rates independently of congestion signals. B-Neck applies max-min fairness as optimization criterion, since it is very often used in traffic engineering as a way of fairly distributing a network capacity among a set of sessions. B-Neck iterates rapidly until converging to the optimal solution and is also quiescent. The fact that B-Neck is quiescent means that it stops creating traffic when it has converged to the max-min rates, as long as there are no changes in the sessions. B-Neck reacts to variations in the environment, and so, changes in the sessions (e.g., arrivals and departures) reactivate the algorithm, and eventually the new sending rates are found and notified. To the best of our knowledge, B-Neck is the first distributed algorithm that maintains the computed max-min fair rates without the need of continuous traffic injection, which can be advantageous, e.g., in energy efficiency scenarios. This paper proposes as novelty two theoretical contributions jointly with an in-depth experimental evaluation of the B-Neck optimization algorithm. First, it is formally proven that B-Neck is correct, and second, an upper bound for its convergence time is obtained. In addition, extensive simulations were conducted to validate the theoretical results and compare B-Neck with the most representative competitors. These experiments show that B-Neck behaves nicely in the presence of sessions arriving and departing, and its convergence time is in the same range as that of the fastest (non-quiescent) distributed max-min fair algorithms. These properties encourage to utilize B-Neck as the basic building block of proactive and anticipatory congestion control protocols. pub

During the last decades, sampling-based algorithms have been used to solve the problem of motion planning. RRT*, as an optimal variant of RRT, provides asymptotic optimality. However, the slow convergence rate and costly initial solution make it inefficient. To overcome these limitations, this paper proposes a modified RRT* algorithm, F-RRT*, which generates a better initial solution and converges faster than RRT*. F-RRT* optimizes the cost of paths by creating a parent node for the random point, instead of selecting it among the existing vertices. The creation process can be divided into two steps, the FindReachest, and CreatNode procedures, which require few calculations, and the triangle inequality is used repeatedly throughout the process, thus, resulting in paths with higher performance than those of RRT*. Since the algorithm proposed in this paper is a tree extending algorithm, its performance can be further enhanced when combined with other sampling strategies. The advantages of the proposed algorithm in the initial solution and fast convergence rate are demonstrated by comparing with RRT*, RRT*-Smart, and Q-RRT* through numerical simulations in this paper.

Bayesian network structure learning is the basis of parameter learning and Bayesian inference. However, it is a NP-hard problem to find the optimal structure of Bayesian networks because the computational complexity increases exponentially with the increasing number of nodes. Hence, numerous algorithms have been proposed to obtain feasible solutions, while almost all of them are of certain limits. In this paper, we adopt a heuristic algorithm to learn the structure of Bayesian networks, and this algorithm can provide a reasonable solution to combine the PC and Particle Swarm Optimization (PSO) algorithms. Moreover, we consider structure priors to improve the performance of our PC-PSO algorithm. Meanwhile, we utilize a new mutation operator called Uniform Mutation by Addition and Deletion (UMAD) and a crossover operator called Uniform Crossover. Experiments on different networks show that the approach proposed in this paper has achieved better Bayesian Information Criterion (BIC) scores than other algorithms.

Leaks and spills of hazardous fluids like petroleum endanger the environment, while remediation costs and penalties imposed when petroleum contaminates the ecosystem affect economics heavily. Therefore, it is crucial to detect any possible symptoms of a leak as soon as possible. Most of existing leak detection techniques require specialized equipment to be used, while purely software-based methods rely solely on data analysis and are very desirable since they can be deployed on petrol stations without any changes to the existing infrastructure. Moreover, such techniques can be considered as complementary to the hardware leak detection systems, as they provide additional security level. In this paper we present the TUBE algorithm, which detects fuel leaks from underground storage tanks, using only standard measurements that are normally registered on petrol stations, i.e. the amount of stored, sold, and delivered fuel. The TUBE algorithm is an autonomous solution capable of making decisions independently as well as supporting human-made decisions and thus can be considered as an expert leak detection system. The TUBE algorithm introduces a new data mining technique for trend detection and cleaning data over time series, which can be easily adapted to any other problem domain. A trend detection technique, called tubes , created for the TUBE algorithm is a novel data analysis method that allows to envelop uncertainties and oscillations in data and produce stable trends. Trend interpretation technique described in this paper has been designed especially for fuel leak detection purposes using our industrial experience. This paper includes a step-by-step usage example of the TUBE algorithm and its evaluation according to the United States Environmental Protection Agency requirements for leakage detection systems (the EPA SIR standard). Such an evaluation involves calculating the probability of detection and the probability of false alarm. The TUBE algorithm has obtained 98.84% probability of detection and 0.07% probability of false alarm while rejecting 42.22% of analyzed datasets due to their uncertainty. Rejecting datasets from analysis is compliant with the EPA SIR standard; however, rejection rate higher than 20% is not acceptable. Therefore we have evaluated the two-phase filtering stage of the algorithm in order to find the best combination of filters as means of data cleaning. Moreover, we have discussed the results pointing at the overall data quality problem, since it is the main cause of rejecting some datasets from the analysis. Finally, the TUBE algorithm has obtained 93.11% probability of detection and 0.73% probability of false alarm for the best combination of all parameters with 15.56% rejection rate, which is acceptable by the EPA SIR standard. The value of probability of detection is not fully compliant with the EPA SIR standard where 95% probability of detection with probability of false alarm lower than 5% is required. We have found that the requirements for the aforementioned probabilities have been completely fulfilled for datasets representing manifolded tank systems but not for single tank datasets. Such a situation was unexpected since manifolded tank systems are generally claimed to be more complex for analysis as they are in fact systems of multiple single tanks directly connected. In this paper we have also measured the time and memory complexity of the TUBE algorithm as well as discussed the issues connected to the TUBE algorithm deployment on petrol stations using our industrial experience in the topic.

This paper presents a novel inertia moment estimation algorithm to enable the Active Force Control Scheme for tracking a wheeled mobile robot (WMR) effectively in a specific trajectory within constrained environments such as on roads or in factories. This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving in a pre-planned path with the presence of noisy measurements. The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. A new Proportional-Derivative Active Force Controller (PD-AFC-LS-QC), employing the use of laser simulator logic and quick compensation loop, has been developed in this paper to robustly reject the noise and disturbances. This controller has three feedback control loops, namely, internal, external and quick compensation loops to compensate effectively the disturbances in the constrained environments. A simulation and experimental studies on WMR path control in two kinds of environments; namely, zigzag and highly curved terrains, were conducted to verify the proposed algorithm and controller which was then compared with other existed control schemes. The results of the simulation and experimental works show the capability of the proposed algorithms and the controller to robustly move the WMR in the constrained environments.

Color image segmentation is the primary step to elicit detailed information of the image with RGB color space. The key to acquire the region of interest is the straightforward, instinctual technique called thresholding. Bi-level thresholding can be assessed concretely for simple images, whereas revealing various classes from a complex image is achieved only through multiple threshold values. Hence, multilevel thresholding with the most propitious maximizing objective functions such as Kapur, Otsu and Minimum Cross Entropy (MCE) to extract the precise threshold are employed in this paper. As the segmentation level increases, execution of non-parametric objective functions leads to exponential increase in computational time. Several researches to enhance the speed of objective functions along with various metaheuristics such as Krill Herd Algorithm (KHA), Teaching-Learning Based Optimization (TLBO) and Cuckoo Search Algorithm (CSA) are performed. The need to unlock the computational complexity drifted our view towards the most powerful, robust, recently introduced metaheuristic Exchange Market Algorithm (EMA). EMA involves exchange of shares among investors to gain profit. The strategic approach of share members in stable and unstable modes of the market results in precise exploration and exploitation. Furthermore, low fitness shareholders gaining experience from their high and moderate counterparts, stands out as an exceptional step to reduce the time consumption. This concept is utilized in our paper for the first time to obtain the exact details from the image without any delay. Empirical outcome of the results indicates that exceptional image segmentation is achieved by EMA in less time compared to extensive search techniques such as KHA, TLBO and CSA. Quantitative and qualitative validation furnished by metrics such as Structural Similarity Index (SSIM), computational time, Peak Signal to Noise Ratio (PSNR) and statistical Wilcoxon test affirm that Otsu, Kapur and MCE based EMA outperforms the existing techniques to analyze the real-world images.

The system uses interval type-2 fuzzy sets in premises and consequences of rules.The system uses several interval type-2 fuzzy implications.The system applies logical interpretation to fuzzy rules.The paper is accompanied by numerical examples.The system can elaborate models with lower errors than type-1 and type-2 systems. Neuro-fuzzy systems have been proved to be an efficient tool for modelling real life systems. They are precise and have ability to generalise knowledge from presented data. Neuro-fuzzy systems use fuzzy sets most commonly type-1 fuzzy sets. Type-2 fuzzy sets model uncertainties better than type-1 fuzzy sets because of their fuzzy membership function. Unfortunately computational complexity of type reduction in general type-2 systems is high enough to hinder their practical application. This burden can be alleviated by application of interval type-2 fuzzy sets. The paper presents an interval type-2 neuro-fuzzy system with interval type-2 fuzzy sets both in premises (Gaussian interval type-2 fuzzy sets with uncertain fuzziness) and consequences (trapezoid interval type-2 fuzzy set). The inference mechanism is based on the interval type-2 fuzzy ukasiewicz, Reichenbach, Kleene-Dienes, or BrouwerGdel implications. The paper is accompanied by numerical examples. The system can elaborate models with lower error rate than type-1 neuro-fuzzy system with implication-based inference mechanism. The system outperforms some known type-2 neuro-fuzzy systems.

We propose a new fully automatic method to detect and segment brain lesions.The method is based on a texture-based and a contour-based algorithm.The algorithm is independent of multi-spectral MRI, and local or global registration. Automatic detection of brain tumors in single-spectral magnetic resonance images is a challenging task. Existing techniques suffer from inadequate performance, dependence on initial assumptions, and, sometimes, the need for manual interference. The research reported in this paper seeks to reduce some of these shortcomings, and to remove others, achieving satisfactory performance at reasonable computational costs. The success of the system described here is explained by the synergy of the following aspects: (1) a broad choice of high-level features to characterize the images texture, (2) an efficient mechanism to eliminate less useful features (3) a machine-learning technique to induce a classifier that signals the presence of a tumor-affected tissue, and (4) an improved version of the skippy greedy snake algorithm to outline the tumors contours. The paper describes the system and reports experiments with synthetic as well as real data.

This paper provides a new approach to feature selection based on the concept of feature filters, so that feature selection is independent of the prediction model. Data fitting is stated as a single-objective optimization problem, where the objective function indicates the error of approximating the target vector as some function of given features. Linear dependence between features induces the multicollinearity problem and leads to instability of the model and redundancy of the feature set. This paper introduces a feature selection method based on quadratic programming. This approach takes into account the mutual dependence of the features and the target vector, and selects features according to relevance and similarity measures defined according to the specific problem. The main idea is to minimize mutual dependence and maximize approximation quality by varying a binary vector that indicates the presence of features. The selected model is less redundant and more stable. To evaluate the quality of the proposed feature selection method and compare it with others, we use several criteria to measure instability and redundancy. In our experiments, we compare the proposed approach with several other feature selection methods, and show that the quadratic programming approach gives superior results according to the criteria considered for the test and real data sets.

In this paper an immuno-inspired algorithm is proposed to generate sequences of data close to the ideal white noise. The motivation to propose the algorithm is that in many cases there is a necessity to generate white noises with a small number of samples, and the pseudo-random generators may fail to perform this task. The proposed algorithm is based on the maximization of the whiteness criterion, clearly defined in this paper, and is different from other immuno-inspired algorithms because it presents an automatic regulation of the suppression threshold, that is an important control variable of the algorithm. This feature allows the proposed algorithm to reach good results, even for different sizes of candidate solutions. To test the proposed algorithm, the results obtained from it are compared to the results obtained from a known pseudo-random generator and it is shown that the solutions obtained with the proposed algorithm are better, for time and frequency domain, if the number of samples required is limited. It is also shown that the proposed algorithm consumes a time comparable to the pseudo-random generator to reach solutions that are better than the ones obtained with that kind of algorithm.

In the framework of regression trees, this paper provides a recursive partitioning methodology to deal with a non-standard response variable. Specifically, either multivalued numerical or modal response of the type histogram will be considered. These data are known as symbolic data, which special cases are classical data, imprecise data, conjunctive data as well as fuzzy data. In spite of pre-processing data in order to deal with standard regression tree methodology, this paper provides, as main contribution, a definition of the impurity measure and of the splitting criterion allowing for building the regression tree for multivalued numerical response variable. We analyze and evaluate the performance of our proposal, using simulated data as well as a real-world case studies.

Clustering is an important field for making data meaningful at various applications such as processing satellite images, extracting information from financial data or even processing data in social sciences. This paper presents a new clustering approach called Gaussian Density Distance (GDD) clustering algorithm based on distance and density properties of sample space. The novel part of the method is to find best possible clusters without any prior information and parameters. Another novel part of the algorithm is that it forms clusters very close to human clustering perception when executed on two dimensional data. GDD has some similarities with today's most popular clustering algorithms; however, it uses both Gaussian kernel and distances to form clusters according to data density and shape. Since GDD does not require any special parameters prior to run, resulting clusters do not change at different runs. During the study, an experimental framework is designed for analysis of the proposed clustering algorithm and its evaluation, based on clustering performance for some characteristic data sets. The algorithm is extensively tested using several synthetic data sets and some of the selected results are presented in the paper. Comparative study outcomes produced by other well-known clustering algorithms are also discussed in the paper. (C) 2016 Elsevier Ltd. All rights reserved.

Chaotic PSO algorithm is proposed to solve NP-hard IPPS problem.Ten chaotic maps are implemented to avoid premature convergence to local optimum.Makespan, balanced level of machine utilization and mean flow time are observed.Five experimental studies show that cPSO outperforms GA, SA, and hybrid algorithm.Scheduling plans are tested by mobile robot within a laboratory environment. Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.

This is the first attempt to solve the SC design problem using IWD metaheuristic.We modify the single-objective IWD meta-heuristic to solve a bi-objective SC design problem.We compare our results to the ones computed by Ant Colony Optimisation (ACO).We solve several instances to show the performance of our hybrid algorithm.Our results outperform the ones computed by ACO. The Intelligent Water Drop (IWD) algorithm is inspired by the movement of natural water drops (WD) in a river. A stream can find an optimum path considering the conditions of its surroundings to reach its ultimate goal, which is often a sea. In the process of reaching such destination, the WD and the environment interact with each other as the WD moves through the river bed. Similarly, the supply chain problem can be modelled as a flow of stages that must be completed and optimised to obtain a finished product that is delivered to the end user. Every stage may have one or more options to be satisfied such as suppliers, manufacturing or delivery options. Each option is characterised by its time and cost. Within this context, multi-objective optimisation approaches are particularly well suited to provide optimal solutions. This problem has been classified as NP hard; thus, this paper proposes an approach aiming to solve the logistics network problem using a modified multi-objective extension of the IWD which returns a Pareto set.Artificial WD, flowing through the supply chain, will simultaneously minimise the cost of goods sold and the lead time of every product involved by using the concept of Pareto optimality. The proposed approach has been tested over instances widely used in literature yielding promising results which are supported by the performance measurements taken by comparison to the ant colony meta-heuristic as well as the true fronts obtained by exhaustive enumeration. The Pareto set returned by IWD is computed in 4źs and the generational distance, spacing, and hyper-area metrics are very close to those computed by exhaustive enumeration. Therefore, our main contribution is the design of a new algorithm that overcomes the algorithm proposed by Moncayo-Martinez and Zhang (2011).This paper contributes to enhance the current body of knowledge of expert and intelligent systems by providing a new, effective and efficient IWD-based optimisation method for the design and configuration of supply chain and logistics networks taking into account multiple objectives simultaneously.

This paper analyses different multi-objective evolutionary algorithms to deal with the Environmental/Economic Load Dispatch (EELD). EELD is formulated as a multi-objective optimization problem in which two competing objectives (fuel cost and pollutants emission) should be optimized simultaneously while fulfilling constraints. Due to the typical process of an evolutionary algorithm (EA), the use of operators applied to individuals of the population might violate constraint rules of the problem. The way in which EAs deal with such constraint rules is an important point and it is directly related to the quality of the generated solutions. One of the contributions of this paper is the analysis of the impact of a repair procedure in four multi-objective EAs. The analyzed approaches are evaluated in eight known instances (with 3, 6, 10, 20 and 40 generators) of the multi-objective EELD. Furthermore, two new instances (with 80 and 120 generators) are proposed and evaluated in this work. Experiments were applied using Dominance Ranking, hypervolume and unary-epsilon indicators, empirical attainment functions and statistical tests, in order to evaluate the algorithms performances. The results point to the consistency of the NSGA-II with repair procedure compared to the literature algorithms, and it outperforms other approaches in most of the considered instances.

Dandelion Algorithm (DA) is a novel swarm intelligent optimization algorithm. In evolutionary process of DA, the quality of the seeds generated by dandelions is uneven, and the excellent seeds are expected to be retained and evaluated, while the poor seeds should be discarded without evaluation. In order to determine whether a seed is excellent or not, an improvement of evolution process of dandelion algorithm with extreme learning machine (ELMDA) is proposed in this paper. In ELMDA, firstly, the dandelion population can be partitioned into excellent dandelions and poor dandelions based on fitness values. Then, the excellent dandelions and poor dandelions are assigned corresponding labels (i.e. +1 if excellent or -1 if poor), which can be regarded as a training set, and the training model is built based on ELM. Finally, the model is applied to classify the seeds as excellent or poor, and the excellent seeds are chosen to participate in evolution process. Meanwhile, the robustness of the proposed algorithm is analyzed in this paper. Experimental results performed on test functions show that the proposed algorithm is competitive to its peers. Moreover, the proposed algorithm is demonstrated on three engineering designed problems, and the results indicate that the proposed algorithm has better performance in solving them.

This paper proposes a novel optimization algorithm for read-coded problems called the Probabilistic Optimization Algorithm (POA). In the proposed algorithm, rather than a binary or integer, a probabilistic representation is used for the individuals. Each individual in the proposed algorithm is a probability density function and is capable of representing the entire search space simultaneously. In the search process, each solution performs as a local search and climbs the local optima, and at the same time, the interaction among the probabilistic individuals in the population offers a global search. The parameters of the proposed algorithm are studied in this paper and their effect on the search process is presented. A structured population is proposed for the algorithm and the effect of different structures is analyzed. The algorithm is used to solve Latin Hyper-cube problem and experimental studies suggest promising results. Different benchmark functions are also used to test the algorithm and results are presented. The analyses suggest that the improvement is more significant for large scale problems.

This paper proposes a computationally efficient optimization algorithm for segmenting colour satellite images.CS algorithm incorporating Mantegna's and McCulloch's method for modeling levy flight is presented.PSO, DPSO, ABC and CS algorithms are compared with the proposed algorithm.All these optimization algorithms are exploited using three different objective functions.Performance assessment metrics demonstrated the improvement in the efficiency of the proposed algorithm. Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for l e ? v y flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna's method for l e ? v y flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications.

The paper presents approximate inversion of fuzzy model with parametrized consequences.The method inverts an existing model.The method uses Ridders algorithm.The paper formulates a theorem for quicker inversion. Fuzzy models are known for their ability of precise data approximation. They can be used in automatic control as controllers. One of the techniques is based on inversion of fuzzy models. Fuzzy models can be inverted exactly or approximately. The exact inversion requires special features of fuzzy models. The approximate methods can be applied to a wider class of fuzzy models. The paper presents an approximate inversion of a fuzzy model with parametrized consequences. This fuzzy model cannot be inverted exactly. The presented approach inverts an existing fuzzy model with respect to any input. The inversion procedure uses the Ridders algorithm. The inversion is fast and does not require a separate training for each inverse input. The convergence of this approach is faster than linear. The paper formulates a theorem that shortens a search interval and may help to accelerate an inversion. The paper is accompanied by numerical examples.

A new scheme to solve the issue of premature convergence of differential evolution.New test functions to evaluate constrained min-max optimization algorithms (CMMOA).An improved CMMOA to achieve a quite satisfied success rate. In system design, the best system designed under a simple experimental environment may not be suitable for application in real world if dramatic changes caused by uncertainties contained in the real world are considered. To deal with the problem caused by uncertainties, designers should try their best to get the most robust solution. The most robust solution can be obtained by constrained min-max optimization algorithms. In this paper, the scheme of generating escape vectors has been proposed to solve the problem of premature convergence of differential evolution. After applying the proposed scheme to the constrained min-max optimization algorithm, the performance of the algorithm could be greatly improved. To evaluate the performance of constrained min-max optimization algorithms, more complex test problems have also been proposed in this paper. Experimental results show that the improved constrained min-max optimization algorithm is able to achieve a quite satisfied success rate on all considered test problems under limited accuracy.

The diversified learning formulas of CHMM parameters are derived.A likelihood-based model averaging estimator is developed.Bearing fault diagnosis is effectively performed. The learning problem of continuous hidden Markov models (CHMMs) is the most critical and challenging one for the application of CHMMs. This paper aims to attack the learning problem of CHMMs by using the diversified gradient descent (DGD) algorithm. The novel learning formula of CHMM parameters, requiring no special form of the objective function and yielding various parameter estimates with different degree of diversity, is derived through dynamically adjusting the iterative procedure according to the gradient change of each parameter. It is the first work for standard CHMM attempting to obtain more local maxima so that the global maximum of the likelihood function of CHMM can be better approximated or even discovered. Hence this paper takes an important step forward in solving the learning problem of CHMM. Furthermore, a likelihood-based model averaging (LBMA) estimator is developed to achieve robust parameter estimation of CHMM based upon the diversiform models attained by the DGD algorithm. The proposed methods are tested on simulation and real-life bearing fault diagnosis problem. The results show that proposed methods perform better in parameter estimation and bearing fault diagnosis compared to the conventional methods.

This paper proposes a robust condition monitoring methodology for rolling element bearings.It employs an empirical mode decomposition (EMD)-based de-noising technique.It also utilizes discrete wavelet packet transform (DWPT)-based envelope analysis.This paper explores the impact of sub-band signals decomposed by the DWPT technique.The proposed scheme outperforms other conventional methods in MPR values. This study presents a robust condition monitoring methodology for rolling element bearings that employs a novel empirical mode decomposition (EMD)-based method to eliminate high-level noise from an acoustic emission (AE) signal and a discrete wavelet packet transform (DWPT)-based envelope analysis technique to effectively search for symptoms of defective bearings. First, the proposed EMD-based de-noising scheme enhances the signal-to-noise ratio by using a Naive Bayes classifier that partitions intrinsic mode functions (IMFs) into noise-dominant and noise-free categories, employing a soft-thresholding-based noise reduction technique for the noise-dominant IMFs, finally obtaining a de-noised acoustic emission (AE) signal via the reconstruction process using both de-noised IMFs and noise-free IMFs. The de-noised AE signal is then decomposed into a set of uniformly spaced sub-bands using three-level DWPT, and the most informative sub-band is determined for early detection of bearing failures. The performance of the proposed condition monitoring scheme is compared with the performance of conventional methods in terms of mean-peak ratio (MPR), which is a metric used to evaluate the degree of defectiveness of the bearings. The experimental results show that the proposed method outperforms the conventional schemes by achieving up to 23.48% higher MPR values, even in a very noisy environment.

We propose an effective hierarchical optimize method HmcDGA.The HmcDGA not only optimizes individuals, but it also optimizes solution space.The HmcDGA can find an optimal solution at low computational cost.The HmcDGA does not require special operations for specific problems.We apply the proposed HmcDGA to the FJSP, and evaluate its effectiveness. In this paper, we propose a new optimization technique, the hierarchical multi-space competitive distributed genetic algorithm (HmcDGA), which is effective for the hierarchical optimization problem. It is an extension of the multi-space competitive distributed genetic algorithm (mcDGA), which was proposed by the authors. The mcDGA efficiently finds an optimal solution with a low computational cost by increasing the number of individuals in a solution space in which it is likely to exist. An optimization method that is divided into several levels of hierarchy is called a hierarchical optimization. Several hierarchical optimization techniques have been proposed, including the hierarchical genetic algorithm (HGA). In hierarchical optimization, a complex problem is divided into a hierarchical collection of simpler problems, and each level is optimized independently. In this way, complex problems can be solved without the need to develop problem-specific operators. However, in the conventional HGA, this results in a high computational cost because the genetic algorithm (GA) is repeated many times at upper and lower level. The HmcDGA is a hybrid of the mcDGA and HGA, and it has some of the advantages of each one; for example, the HmcDGA can find an optimal solution at low computational cost and without requiring special operations. This allows it to be applied to a wide variety of optimization problems. Therefore, the HmcDGA may become the powerful optimization algorithm that can solve various problems. In this paper, we apply the proposed HmcDGA to the flexible job-shop scheduling problem (FJSP) which is one of the complex combinational optimization problem and confirm its effectiveness. Simulation results show that the HmcDGA can find solutions that are comparable to those found by using GAs developed specifically for the FJSP, the HmcDGA is not required a lot of computational costs comparing to the HGA.

During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm.

The proposition of novel evolutionary algorithm for flow design in elastic networks.Evolutionary methods hybridization.Problem dedicated mechanisms.The detailed analysis of fitness function evaluation number and computation load dependence.Successful solving problem of large solution space. The fast social and economic development observed in the recent years brings up new challenging optimization problems. These problems are often very hard not only because of their computational complexity, but also due to their enormous solution space size. Therefore, this paper proposes an effective optimization method, based on the novel Multi Population Pattern Searching (MuPPetS) Algorithm, to solve optimization problems characterized with very large solution space. As a case study problem, we focus on the problem of routing and spectrum allocation with joint anycast and unicast traffic demands that arises in the field of optical networks optimization. The proposed method is adjusted to the problem with proper solution encoding, hybridization using a local search algorithm, and dedicated mechanisms necessary to improve method convergence. The above adjustments are required to make the method effective against test cases with solution space size of up to 103700 points (sets of values of the choice variables). The paper compares the performance of the proposed method with other reference methods known from the literature. Another key contribution of this paper is presentation of the complicated dependency between fitness function evaluation number (FFE) and real computation load, which are used to evaluate effectiveness of the proposed technique. The analysis is supported with proper empirical tests and their analysis.