Search

The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction.The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement.This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of digital IIR filter design. Two novel modifications are proposed to Particle Swarm Optimization and validated through novel application for design of IIR filter. First modification is based on quantum mechanics and proved to yield a better performance. The second modification is to take care of time dependency character of the constriction factor. Extensive simulation results validate the superior performance of proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Adaptation of mobile robot controllers commonly requires the computation of optimal points of operation. Specifically, for miniature mobile robots with serious computational limitations, that are typical of embedded systems, one of the main challenges is the adaptation of efficient computational methods in order to find solutions of complex optimization problems, which demand large execution times. This drawback compels the design of high-performance parallel optimization algorithms which must run over embedded system platforms. This paper describes how adequate hardware implementations of the Particle Swarm Optimization (PSO) algorithm can be useful for real time adaptation of mobile robot controllers. For achieving this, a new architecture is proposed, which is based on an FPGA implementation of the opposition-based learning (OBL) approach applied to the PSO (for short HPOPSO), and which explores the intrinsic parallelism of this algorithm in order to adjust the weights of a neural robot controller in real time according to desired behaviors. The proposed HPOPSO was applied to the learning-from-demonstration problem in which a teacher performs executions of the desired behavior. Effectiveness of the proposed architecture was demonstrated by numerical simulations and the feasibility of the adaptive behavior of the neural robot controller was confirmed for two obstacle avoidance case studies that were preserved when one or more failures on the distance sensors occur. The HPOPSO, which uses the OBL technique, improves the quality of the solutions in comparison with the standard PSO. Comparisons of the adaptation time between hardware and software approaches have demonstrated the suitability of the FPGA implementation of the proposed HPOPSO for attending specific requirements of embedded system applications. [Copyright &y& Elsevier]

This paper addresses the synthesis of the three-dimensional (3D) radiation patterns of the time-modulated conformal arrays. Due to the nature of periodic time modulation, harmonic radiation patterns are generated at the multiples of the modulation frequency in time-modulated arrays. Thus, the optimization goal of the time-modulated conformal array includes the optimization of the sidelobe level at the operating frequency and the sideband levels (SBLs) at the harmonic frequency, and the design can be regarded as a multiobjective problem. The multiobjective particle swarm optimization (MOPSO) is applied to optimize the switch-on instants and pulse durations of the time-modulated conformal array. To significantly reduce the optimization variables, the modified Bernstein polynomial is employed in the synthesis process. Furthermore, dual polarized patch antenna is designed as radiator to achieve low cross-polarization level during the beam scanning. A 12 x 13 (156)-element conical conformal microstrip array is simulated to demonstrate the proposed synthesis mechanism, and good results reveal the promising ability of the proposed algorithm in solving the synthesis of the time-modulated conformal arrays problem. [ABSTRACT FROM AUTHOR]

The modulus of asphalt pavement material is a necessary parameter for the design, strength measuring and stability evaluation of asphalt pavement. To get more precise test data for asphalt pavement material modulus, a new modulus back calculation method is proposed in this article, named as the Firefly Asphalt Back Calculation Method (FABCM). This novel method uses the firefly optimization algorithm, which is a kind of particle swarm intelligence algorithm imitating the information transfer process among fireflies. To demonstrate the reliability and stability of FABCM, and to study the feasibility of multi-parameter modulus back calculation methods, this article used theoretical deflection curves calculated by BISAR3.0 and the actual measurement data of deflection curves and vertical pressures on the subgrade top surfaces on the full-scale test circular track in the Research Institute of Highway, Ministry of Transport (RIOHTrack) to conduct a modulus back calculation. The results show that FABCM only takes 0.5–1 s for each calculation, and the back calculation errors in the verification of FABCM are mostly smaller than 1%, which means that the firefly optimization algorithm was modified effectively in this article. Moreover, this article also indicates some key factors influencing the accuracy of modulus back calculation, and several reasonable suggestions to the application of modulus back calculation. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers simultaneous optimization of strategic design and distribution decisions for three-echelon supply chain architecture consisting of following three players; suppliers, production plants, and distribution centers (DCs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to distribution centers, so as to minimize the combined facility location, production, inventory, and shipment costs and maximize fill rate. To achieve this, three-echelon network model is mathematically represented and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization algorithm (MOHPSO). This heuristic incorporates non-dominated sorting (NDS) procedure to achieve bi-objective optimization of two conflicting objectives. The applicability of proposed optimization algorithm was then tested by applying it to standard test problems found in literature. On achieving comparable results, the approach was applied to actual data of a pump manufacturing industry. The results show that the proposed solution approach performs efficiently. [Copyright &y& Elsevier]

Abstract: Multiuser scheduling is an important aspect in the performance optimization of a wireless network since it allows multiple users to access a shared channel efficiently by exploiting multiuser diversity. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. In this paper, a more realistic imperfect CSI feedback, in the form of a finite set of Channel Quality Indicator (CQI) values, is assumed as specified in the HSDPA standard. A mathematical model of the problem is developed for use in the optimization process. A hybrid heuristic approach based on particle swarm optimization and simulated annealing is used to solve the problem. Simulation results indicate that the hybrid approach outperforms individual implementations of both simulated annealing and particle swarm optimization. [Copyright &y& Elsevier]

Abstract: Optimization in dynamic environment is considered among prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel awakening–sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency. [Copyright &y& Elsevier]

Abstract: The bacterial foraging optimization (BFO) algorithm is a popular stochastic, population-based optimization technique that can be applied to a wide range of problems. Two are the major issues the BFO algorithm is confronted with: first, the foraging mechanism of BFO might in some cases induce the attraction of bacteria gathered near the global optimum by bacteria gathered to local optima, thus slowing down the whole population convergence. Second, BFO is susceptible to the curse-of-dimensionality, which makes it significantly harder to find the global optimum of a high-dimensional problem, compared to a low-dimensional problem with similar topology. In this paper, we introduce a novel BFO-based optimization algorithm aiming to address these issues, the synergetic bacterial swarming optimization (SBSO) algorithm. Our novel approach consists of: (i) the introduction of the swarming dynamics of the particle swarm optimization algorithm in the context of BFO, in order to ameliorate the convergence issues of the BFO bacteria foraging mechanism; and (ii) the utilization of multiple populations to optimize different components of the solution vector cooperatively, so as to mitigate the curse-of-dimensionality issues of the algorithm. We demonstrate the efficacy of our approach on several benchmark optimization problems. [Copyright &y& Elsevier]

Abstract: This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems. [Copyright &y& Elsevier]

Abstract: This paper presents a real-value version of particle swarm optimization (PSO) for solving the open vehicle routing problem (OVRP) that is a well-known combinatorial optimization problem. In OVRP a vehicle does not return to the depot after servicing the last customer on a route. A particular decoding method is proposed for implementing PSO for OVRP. In the decoding method, a vector of the customer’s position is constructed in descending order. Then each customer is assigned to a route with taking into account feasibility conditions. Finally one-point move has been applied on constructed routes that seem promising to result in a better solution. Experimental evaluations on benchmark data sets demonstrate the competitiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Purpose: Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence. Method: Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function. Results: The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers. [Copyright &y& Elsevier]

Abstract: A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very complex. Unlike PNN the proposed network needs to generate the partial description for a single layer. The discrete PSO (DPSO) is used to select a relevant set of PDs as well as features with a hope to get better accuracy, which are in turn fed to the output neuron. The weights associated with the links from hidden to output neuron is optimized by PSO for continuous domain (CPSO). Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of this model both in processing time and accuracy, is encouraging for harnessing its power in domain with large and complex data particularly in data mining area. [Copyright &y& Elsevier]

Abstract: The Artificial Bee Colony (ABC) algorithm is inspired by the behavior of honey bees. The algorithm is one of the Swarm Intelligence algorithms explored in recent literature. ABC is an optimization technique, which is used in finding the best solution from all feasible solutions. However, ABC can sometimes be slow to converge. In order to improve the algorithm performance, we present a modified method for solution update of the onlooker bees in this paper. In our method, the best feasible solutions found so far are shared globally among the entire population. Thus, the new candidate solutions are more likely to be close to the current best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, in each iteration, we adjust the radius of the search for new candidates using a larger radius earlier in the search process and then reduce the radius as the process comes closer to converging. Finally, we use a more robust calculation to determine and compare the quality of alternative solutions. We empirically assess the performance of our proposed method on two sets of problems: numerical benchmark functions and image registration applications. The results demonstrate that the proposed method is able to produce higher quality solutions with faster convergence than either the original ABC or the current state-of-the-art ABC-based algorithm. [ABSTRACT FROM AUTHOR]

Abstract: As a powerful optimization algorithm, particle swarm optimization (PSO) has been widely applied to power system researches. However, most existing applications of PSO can only be implemented offline. The difficulties of online implementation mainly come from the unavoidable lengthy simulation time to evaluate a candidate solution. Recently, PSO was implemented online that can identify parameters in a motor control systems. In this paper, the real-time PSO (RT-PSO) based identification technique is applied to cancel current harmonics in power systems. By transforming the identification problem to optimization problem, RT-PSO can simultaneously identify four parameters associated with fundamental current from measurement. In this way, there is no need to identify the fundamental frequency separately or construct fundamental signal from identified harmonic information. The identification algorithm can be applied to three-phases independently, even for unbalanced system or single-phase system. The identified fundamental signal is then used as the reference for current harmonics cancellation. The RT-PSO based harmonic cancellation is realized with an active filter and used to compensate harmonic current created by a nonlinear load. Simulation results demonstrate that the RT-PSO algorithm can provide accurate identification of the fundamental current which in turn will result in good harmonic cancellation performance. As a capable online optimization technique, RT-PSO can be extensively applied to many optimization and control problems. [ABSTRACT FROM AUTHOR]

Abstract: Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature. [Copyright &y& Elsevier]

In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs). The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (Eas) and Swarm Intelligence (SI). In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA) and Difference Evolution (DE), and one SI algorithm, namely, Particle Swarm Optimization (PSO), on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient. [ABSTRACT FROM AUTHOR]

Abstract: Back analysis is commonly used in identifying geomechanical parameters based on the monitored displacements. Conventional back analysis method is not capable of recognizing non-linear relationship involving displacements and mechanical parameters effectively. The new intelligent displacement back analysis method proposed in this paper is the combination of support vector machine, particle swarm optimization, and numerical analysis techniques. The non-linear relationship is efficiently represented by support vector machine. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, a global optimum search on the obtained SVMs by particle swarm optimization can lead to the geomechanical parameters identification effectively. [Copyright &y& Elsevier]

Abstract: In multi-objective particle swarm optimization (MOPSO) algorithms, finding the global optimal particle () for each particle of the swarm from a set of non-dominated solutions is very difficult yet an important problem for attaining convergence and diversity of solutions. First, a new Pareto-optimal solution searching algorithm for finding the in MOPSO is introduced in this paper, which can compromise global and local searching based on the process of evolution. The algorithm is implemented and is compared with another algorithm which uses the Sigma method for finding on a set of well-designed test functions. Finally, the multi-objective optimal regulation of cascade reservoirs is successfully solved by the proposed algorithm. [Copyright &y& Elsevier]

Abstract: A particle is treated as a whole individual in all researches on particle swarm optimization (PSO) currently, these are not concerned with the information of every particle’s dimensional vector. A visual modeling method describing particle’s dimensional vector behavior is presented in this paper. Based on the analysis of visual modeling, the reason for premature convergence and diversity loss in PSO is explained, and a new modified algorithm is proposed to ensure the rational flight of every particle’s dimensional component. Meanwhile, two parameters of particle-distribution-degree and particle-dimension-distance are introduced into the proposed algorithm in order to avoid premature convergence. Simulation results of the new PSO algorithm show that it has a better ability of finding the global optimum, and still keeps a rapid convergence as with the standard PSO. [Copyright &y& Elsevier]

Abstract: Middle-long forecasting of electric power load is crucial to electric investment, which is the guarantee of the healthy development of electric industry. In this paper, the particle swarm optimization (PSO) is used as a training algorithm to obtain the weights of the single forecasting method to form the combined forecasting method. Firstly, several forecasting methods are used to do middle-long power load forecasting. Then the upper forecasting methods are measured by several indices and the entropy method is used to form a comprehensive forecasting method evaluation index, following which the PSO is used to attain a combined forecasting method (PSOCF) with the best objective function value. We then obtain the final result by adding all the results of every single forecasting method. Taking actual load data of a power grid company in North China as a sample, the results show that PSOCF model improves the forecasting precision compared to the traditional models. [Copyright &y& Elsevier]

Abstract: This paper presents a co-evolutionary particle swarm optimization (CPSO) algorithm to solve global nonlinear optimization problems. A new co-evolutionary PSO (CPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, based on the theory of extrapolation, the induction of evolving direction is enhanced by adding a co-evolutionary strategy, in which the particles make full use of the information each other by using gene-adjusting and adaptive focus-varied tuning operator. Infeasible degree selection mechanism is used to handle the constraints. A new selection criterion is adopted as tournament rules to select individuals. Also, the infeasible solution is properly accepted as the feasible solution based on a defined threshold of the infeasible degree. This diversity mechanism is helpful to guide the search direction towards the feasible region. Our approach was tested on six problems commonly used in the literature. The results obtained are repeatedly closer to the true optimum solution than the other techniques. [Copyright &y& Elsevier]

Abstract: This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies has been inspired by natural swarms such as the school of fish or the flock of birds. In addition to the target search using the above methods, a hierarchy for the coordination of a swarm of robots has been proposed. The robustness of both strategies is evaluated for failures or loss in swarm members. Results are presented with both strategies and comparisons of their performance are carried out against a greedy search algorithm. [Copyright &y& Elsevier]

For classification tasks in machine learning, this paper proposes a brand-new wrapper feature selection algorithm prototype named recursive elimination–election (REE), which is conceived in a simple but exquisite structure inspired by the recursion technique in computer science. Prevalent metaheuristic methods such as differential evolution (DE), particle swarm optimization (PSO), etc., from evolutionary computation (EC) and swarm intelligence (SI) communities have recently been widely applied to feature selection research, but suffer from severe drawbacks including but not limited to low-efficient binary representation transformation, poor population diversity, excessive control parameter adjustments and sophisticated mechanisms. Instead, REE is organically constructed with an ordinary subset representation of feature indexes, simple operators, getting rid of extra control parameters. Specifically, REE is assembled of two basic recursive sub-algorithms, i.e., recursive random bisection elimination (RRBE) and recursive greedy binary election (RGBE), which somewhat embody the idea of "divide-and-conquer". By inspecting smaller and potential feature subsets in recursive ways, better subsets are returned automatically. A comprehensive experimental study was conducted on 14 UCI and ASU benchmark datasets with feature sizes ranging from dozens to thousands by using REE together with 6 state-of-the-art metaheuristic algorithms for comparison. The results show that the proposed REE has competitive search ability for feature selection problems, and it is especially prominent in handling high-dimensional datasets. Therefore, REE is promising to become a wrapper feature selection search paradigm with low solution cost and high efficiency. • A brand-new wrapper feature selection algorithm based on recursion technique. • Only using set operations makes the algorithm easier than metaheuristic ones. • The algorithm does not get stuck into local optima, is able to explore more space. • More effective and scalable to handle high-dimensional datasets. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design and its realization of poly-phase induction motor using Quadratic Interpolation based Particle Swarm Optimization (QI-PSO). The optimization algorithm considers the efficiency, starting torque and temperature rise as objective function (which are considered separately) and nine performance related items as constraints. The QI-PSO algorithm was implemented on a test motor and the results are compared with the Simulated Annealing (SA) technique, Standard Particle Swarm Optimization (SPSO), and normal design. Some benchmark problems are used for validating QI-PSO. From the test results QI-PSO gave better results and more suitable to motor's design optimization. Optimized variables are realized by PC-IMD (Induction Motor Drives) of SPEED (Scottish Power Electronics and Electric Drives) software. C++ code is used for implementing entire algorithms. [ABSTRACT FROM AUTHOR]

How to manage trade-off between exploitation and exploration in Particle Swarm Optimization (PSO) for efficiently solving various optimization problems is an important issue. In order to prevent premature convergence in PSO search, this paper proposes a new method, Particle Swarm Optimization with Diversive Curiosity (PSO/DC). A key idea of the proposed method is to introduce a mechanism of diversive curiosity into PSO for preventing premature convergence and for managing the exploration-exploitation trade-off. Diversive curiosity is represented by an internal indicator that detects marginal improvement of a swarm of particles for certain number of iterations, and forces them to continually explore an optimal solution to a given optimization problem. Applications of the proposed method to a 2-dimensional optimization problem well demonstrate its effectiveness. Our experimental results indicate that the performance (100%) by the proposed method is superior in terms of success ratio to that (60%) by the PSO model optimized by EPSO, and basically accord with the finding called "the zone of curiosity" in psychology. [ABSTRACT FROM AUTHOR]

Based on the standard particle swarm optimization (PSO) algorithm together with the widely used dynamic niche technology, this paper presents a new variation combined with the dynamic niche sharing technique on the basis of traditional PSO algorithm. We proposed a cooperative particle swarm optimization model with cooperative multi-population. Applications are given on creative conceptual architectural design. [Copyright &y& Elsevier]

Feature Selection (FS) phase is crucial in the Event Detection (ED) model. Several studies have captured the most informative features using various filter and wrapper FS methods. Recently, FS methods based on swarm intelligence algorithms have been employed to determine the relevant features. Nevertheless, ED from sparse and high-dimensional feature space resulting from a massive number of news documents with different text lengths is a challenging task. Such feature space consists of redundant, irrelevant, and noisy data, which misguide the detection process and substantially, affect the reliability of the ED model. Hence, this study proposes a novel Binary Bat Algorithm (BBA) and Markov Clustering Algorithm (MCL) to improve the performance of the ED model. To the best of our knowledge, BBA is employed for the first time in this study in the context of the ED field. The proposed method is tested on 10 benchmark datasets and 2 primary Facebook news datasets using the average of several evaluation metrics such as F-measure (F), Precision (PR), Recall (R), and Selected Feature Ratio (SFR). Comparative experiments against the basic MCL, Binary versions of the Genetic Algorithm and Particle Swarm Optimization are implemented in this study. The empirical results proved that BBA-MCL outperforms other methods on most datasets based on F and PR metrics. Furthermore, the statistical results confirmed that the BBA-MCL FS method has significantly enhanced MCL performance with p-value = 0.003, by generating the most informative features. Ultimately, this work concludes that BBA-MCL obtains significant features and effectively detects real-world events from heterogeneous news text documents. [ABSTRACT FROM AUTHOR]

As the curse of dimensionality, band selection is utilized to choose part of feature information from hyperspectral image (HSI) and achieve the highest classification accuracy as possible. It is considered as a non-polynomial hard problem and difficult to be fully solved within the effective period, especially for traditional swarm optimization. Gray wolf optimizer (GWO) is a newly proposed swarm intelligence algorithm based on heuristic search, and chaotic operation helps the algorithm jump out of the local optima. In the paper, a novel band selection approach based on a modified GWO (MGWO) is presented to reduce the data dimension of HSIs, and chaotic operation is utilized to set the index of gray wolves. In addition, the weight of bands is updated by the best and worst fitness values as the iteration of MGWO, and evaluates the quality of each band. Experimental results illustrate that it is better than other state-of-the-art band selection approaches, and satisfied band subsets with the combination from 10% to 25% total number of bands are obtained with higher classification accuracy. • A MGWO is proposed to set the index of gray wolves and the parameter of transform function. • The weight of each band is updated by the best and worst fitness values as iteration. • A fixed number of bands is selected by the rank of weight to build band subset for HSI dataset. • A band selection approach based on MGWO is proposed, and OA keeps in satisfactory with fewer bands. [ABSTRACT FROM AUTHOR]

As an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains two crucial problems. To overcome these drawbacks of PSO, a hybrid particle swarm optimization with crisscross learning strategy (PSO-CL) algorithm is proposed in this paper. In PSO-CL, in order to well balance the global exploration and local exploitation capabilities of PSO, a search direction adjustment mechanism based on subpopulation division operation is proposed. Meantime, to avoid the premature convergence and enhance the global search ability, a crossover-based comprehensive learning strategy (CCL) is adopted. Additionally, a stochastic example learning strategy (SEL) is introduced, which can assist collective information to be spread among separate sub-swarms, improve the local exploitation ability of the algorithm. 15 classic benchmark functions, CEC2017 test suite and two real-world optimization problems are utilized to verify the promising performance of PSO-CL, experimental results and statistical analysis indicate that PSO-CL has competitive performance compared with state-of-the-art PSO variants. [Display omitted] • A search direction adjustment mechanism based on subpopulation division operation. • A crossover-based comprehensive learning strategy is presented, which can enhance the diversity of swarm. • A stochastic example learning strategy is introduced to further enhance the convergence precise. [ABSTRACT FROM AUTHOR]

• Biologically inspired heuristics for target search lack modularity and modifiability. • The proposed hyper-heuristic is based on modular and adaptive swarm behavior. • Combining stigmergy, flocking and evolution outperforms bio-inspired heuristics. Target search aims to discover elements of various complexity in a physical environment, by minimizing the overall discovery time. Different swarm intelligence algorithms have been proposed in the literature, inspired by biological species. Despite the success of bio-inspired techniques (bio-heuristics), there are relevant algorithm selection and parameterization costs associated with every new type of mission and with new instances of known missions. In this paper, evolutionary optimization is proposed for achieving significant improvements of the mission performance. Although adaptive, the logic of bio-heuristics is nevertheless constrained by models of biological species. To generate more adaptable logics, a novel design approach based on hyper-heuristics is proposed, in which the differential evolution optimizes the aggregation and tuning of modular heuristics for a given application domain. A modeling and optimization testbed has been developed and publicly released. Experimental results on real-world scenarios show that the hyper-heuristics based on stigmergy and flocking significantly outperform the adaptive bio-heuristics. [Display omitted] [ABSTRACT FROM AUTHOR]

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel $N$ -state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions. [ABSTRACT FROM AUTHOR]

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy's flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy's flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars' attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO. [ABSTRACT FROM AUTHOR]

Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance. [ABSTRACT FROM AUTHOR]

As the technology of the Internet of Things (IoT) and mobile edge computing (MEC) develops, more and more tasks are offloaded to the edge servers to be computed. The offloading strategy performs an essential role in the progress of computation offloading. In a general scenario, the offloading strategy should consider enough factors, and the strategy should be made as quickly as possible. While most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions. Then, grey wolf optimizer (GWO) is introduced to solve the improved model. To obtain better performance, we proposed an algorithm hybrid whale optimization algorithm (WOA) with GWO named GWO-WOA. And the improved algorithm is tested on our model. Finally, the results obtained by GWO-WOA, GWO, WOA, particle swarm optimization (PSO), and genetic algorithm (GA) are discussed. The results have shown the advantages of GWO-WOA. [ABSTRACT FROM AUTHOR]

Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named "Lévy flight") with occasional "big" steps. This characteristic makes Lévy flight especially useful for performing large "jumps" that allow the search to escape from a local optimum and restart in a different region of the search space. In this paper, we investigate this idea by applying Lévy flight to Jaya, a simple yet effective Swarm Intelligence optimization algorithm recently proposed in the literature. We perform experiments on the CEC 2014 benchmark as well as five industrial optimization problems taken from the CEC 2011 benchmark, and compare the performance of the proposed Lévy flight Jaya Algorithm (LJA) against several state-of-the-art algorithms for continuous optimization. Our numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems. • We present the Lévy flight Jaya Algorithm (LJA) for continuous optimization. • We evaluate the effect of the β parameter on LJA. • LJA consistently outperforms the original Jaya on most optimization problems. • We compare LJA with state-of-the-art algorithms on benchmark/industrial problems. [ABSTRACT FROM AUTHOR]

Classical clustering algorithms like K-means often converge to local optima and have slow convergence rates for larger datasets. To overcome such situations in clustering, swarm based algorithms have been proposed. Swarm based approaches attempt to achieve the optimal solution for such problems in reasonable time. Many swarm based algorithms such as Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA), Black Hole Algorithm (BHA), Bat Algorithm (BA) Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Artificial Bee Colony (ABC) etc have been successfully applied to many non-linear optimization problems. In this paper, an algorithm is proposed which hybridizes Chaos Optimization and Flower Pollination over K-means to improve the efficiency of minimizing the cluster integrity. The proposed algorithm referred as Chaotic FPA (CFPA) is compared with FPA, CSA, BHA, BA, FFA, and PSO over K-Means for data clustering problem. Experiments are conducted on sixteen benchmark datasets. Algorithms are compared on four different performance parameters — cluster integrity, execution time, number of iterations to converge (NIC) and stability. Results obtained are analyzed statistically using Non-parametric Friedman test. If Friedman test rejects the Null hypothesis then pair wise comparison is done using Nemenyi test. Experimental Result demonstrates the following: (a) CFPA and BHA have better performance on the basis of cluster integrity as compared to other algorithms; (b) Prove the superiority of CFPA and CSA over others on the basis of execution time; (c) CFPA and FPA converges earlier than other algorithms to evaluate optimal cluster integrity; (d) CFPA and BHA produce more stable results than other algorithms. • A proposed algorithm which hybridizes chaos and FPA over K-means for clustering. • Chaotic FPA (CFPA) is compared with algorithms FPA, CSA, BHA, BA, FFA, and PSO. • For cluster integrity, CFPA and BHA have better performance as compared to the others. • Cluster Integrity has been improved by 3.17% as compared to previous study for dataset D16. • CFPA and CSA are significantly superior to other algorithms on the basis of execution time. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD. [ABSTRACT FROM AUTHOR]

• Four novel variants of the Cuckoo-Search (CS) algorithm are developed. • Parameter adaptation, dynamic penalties and hybridization with BSA are used. • Enhanced CS variants outperform original CS and other metaheuristic algorithms. • CS variants become more efficient as problem size and/or complexity are increased. • Using dynamic penalties yields better final designs and faster convergence. Although Cuckoo Search (CS) is a quite new nature-inspired metaheuristic optimization algorithm, it has been extensively used in engineering applications, since it has been proven very efficient in solving complex nonlinear problems. In this paper, efficient modifications have been made to the original CS algorithm to enhance its efficiency and robustness. More specifically, constant parameters of the algorithm, such as the probability of the alien egg being discovered by the host bird and the step size of Levy flights have been dynamically tuned. In addition, static and dynamic penalty functions are introduced within the optimization formulation. Finally, a hybrid optimization approach is developed to combine the advantages of CS with those of Bird Swarm Algorithm (BSA). Benchmark problems, widely used in relevant studies, have been solved and the obtained solutions are compared with those previously reported using the standard CS algorithm and other popular evolutionary optimization techniques (i.e., Genetic Algorithms, Particle Swarm Optimization, etc.). [ABSTRACT FROM AUTHOR]

The existing MOPSOs face a great challenge in dealing with many-objective problems, due to the low discriminability of particles in many-objective spaces, which will affect the selection of leaders, thereby deteriorating the effectiveness of the algorithm. By using the property of domination, with the mapping of grid coordinates, this paper presents a framework of grid-based ranking scheme to sort the particles in grid space, which can select the swarm leaders (g b e s t , p b e s t) efficiently in MOPSO and enhance the convergence. Moreover, the clustering operation is conducted to update and maintain the external archive in the grid space to output a more diverse Pareto front. The performance of the proposed algorithm is verified by benchmark comparisons with several state-of-the-art MOPSOs and MOEAs. The DTLZ and WFG test suites with 4 to 10 objectives are used to assess the performance of our proposal. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed. Additionally, we discuss the influence of grid partition on efficiency, which verifies the effectiveness of the proposal from the other aspect. • A grid dominance ranking technique is designed to enhance the discriminability of particles in many-objective particle swarm optimization. With this ranking scheme, the swarm leaders (gbest, pbest) can be picked explicitly, so that the updating of particles can undergo effectively for MaOPs. • On the basis of grid space, a clustering operation is conducted to update and maintain the external archive. • In light of grid dominance ranking and grid clustering, a novel algorithm named MOPSO/ GDR is designed for solving MaOPs. [ABSTRACT FROM AUTHOR]

Stochastic nature-inspired population-based algorithms are very powerful tools for solving stationary and deterministic, NP-hard optimization problems. These algorithms have rarely been applied to real-world dynamic and uncertain optimization due to their complexity. In this paper, this kind of algorithms were ported onto real hardware (i.e., the velocity controller of a one degree of freedom robot mechanism), where they were used to control the behavior of a non-linear system online. This means that the feedback response from the system must be less than 5 ms. Due to the complexity of the fitness function evaluation, a surrogate linear model was used, implemented as a single-layer artificial neural network, consisting of two phases: learning and simulation. In the first phase, the model of the nonlinear plant is learned during online operation, while in the second, the value of the fitness function needed by the optimization algorithms is predicted. Six algorithms were compared with the PI-controller in our experimental work. This were: classical evolution strategies, contemporary evolution strategies, differential evolution, self-adaptive differential evolution, particle swarm optimization, and the bat algorithm. The results showed that the algorithms outperformed PI-controller in the sense of stability, flexibility and adaptability. • Nature-inspired optimization algorithms often used for offline optimization. • Rarely used for online problem solving due to complexity. • Online adaptive controller developed using nature-inspired algorithm and ANN. • Online adaptive controller tested at control of non-linear plant. • Online adaptive controller performed better than classic PI-controller. [ABSTRACT FROM AUTHOR]

• Propose a novel FS method, called ISSAFD, which improves Salp Swarm Algorithm (SSA). • ISSAFS enhanced the followers in SSA using SCA and Disrupt operator (DO). • Evaluating the influence of the operators of SCA on the behavior of leaders in SSA. • Comparing the performance of ISSAFD with swarm intelligence (SI). • The proposed ISSAFD provided better results in terms of performance measures. Features Selection (FS) plays an important role in enhancing the performance of machine learning techniques in terms of accuracy and response time. As FS is known to be an NP-hard problem, the aim of this paper is to introduce basically a new variant of Salp Swarm Optimizer (SSA) for FS (called ISSAFD (Improved Followers of Salp swarm Algorithm using Sine Cosine algorithm and Disrupt Operator), that updates the position of followers (F) in SSA using sinusoidal mathematical functions that were inspired from the Sine Cosine Algorithm (SCA). This enhancement helps to improve the exploration phase and to avoid stagnation in a local area. Moreover, the Disruption Operator (D op) is applied for all solutions, in order to enhance the population diversity and to maintain the balance between exploration and exploitation processes. Two other variants of SSA are developed based on SCA called ISSALD (Improved Leaders of Salp swarm Algorithm using Sine Cosine algorithm and Disrupt Operator) and ISSAF (Improved Followers of Salp swarm Algorithm using Sine Cosine algorithm). The updating process in consists to update the leaders (L) position by SCA and applying (D op), whereas in ISSAF, the D op is omitted and the position of followers is updated by SCA. Experimental results are evaluated on twenty datasets where four of them represent high dimensionality with a small number of instances. The obtained results show a good performance of ISSAFD in terms of accuracy, sensitivity, specificity, and the number of selected features in comparison with other metaheuristics (MH). [ABSTRACT FROM AUTHOR]

• Hybrid distributed SI - NM algorithms is investigated for DOA estimation in WSN. • Distributed MEGWO-NM Algorithm reduces communication overhead and computation time. • Combiner coefficients based on degree of sensor node is proposed. • Distributed CRLB is derived to compare the performance of hybrid algorithms. Distributed direction of arrival (DOA) estimation based on maximum likelihood (ML) is an energy-efficient source localization technique that is vital to systems such as wireless sensor networks (WSN). Due to the multimodal nature of the ML function, the distributed approach uses swarm intelligence (SI) algorithms. However, this approach has slow convergence and incurs significant communication overhead for more than two sources. Hence, to obtain accurate DOA estimates with faster convergence, this paper proposes a distributed hybrid version of SI and Nelder-Mead (NM) simplex algorithm. In this approach, a modified evolutionary population dynamics based distributed Grey Wolf optimization SI algorithm is proposed to obtain initial DOA estimates. Then NM provides accurate DOA estimates with faster convergence and thereby reduces the communication overhead. Detailed simulation analysis shows that by using Quasi-Opposition based population initialization and sensor node degree combiner coefficients, the proposed distributed hybrid algorithm converges to theoretical Cramer-Rao lower bound with small communication overhead. Thus DOA estimation can be performed in an energy-efficient way by incorporating a computationally intelligent distributed hybrid approach. [ABSTRACT FROM AUTHOR]

The conventional thresholding methods are very efficient for bi-level thresholding, but the computational complexity may be excessively high for color image multilevel thresholding. Color image multilevel thresholding segmentation can be considered as a constrained optimization problem, therefore swarm intelligence algorithms are widely used to reduce the complexity. In this paper, an efficient krill herd (EKH) algorithm is proposed to search optimal thresholding values at different level for color images and the Otsu's method, Kapur's entropy and Tsallis entropy are employed as objective functions. Seven different algorithms, KH without any genetic operators (KH I), KH with crossover operator (KH II), KH with crossover and mutation operators (KH IV), modified firefly algorithm (MFA), modified grasshopper optimization algorithm (MGOA), bat algorithm (BA) and water cycle algorithm (WCA), are compared with the EKH algorithm. Experiments are performed on ten color benchmark images in terms of optimal threshold values, objective values, PSNR, SSIM and standard deviation of the objective values at different levels. The experimental results show that the presented EKH algorithm is superior to the other algorithms for color image multilevel thresholding segmentation. On the other hand, Kapur's entropy is found to be more accurate and robust for color image multilevel thresholding segmentation. • An efficient krill herd (EKH)algorithm is proposed. • EKH algorithm is used for color image multilevel thresholding segmentation. • Otsu, Kapur's entropy and Tsallis entropy are used as objective functions. • EKH algorithm is an effective color image multilevel thresholding method. [ABSTRACT FROM AUTHOR]