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Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) can ensure the network security by identifying network intrusions according to the abnormal traffic data. However, the intrusion detection data has the problem of high dimensionality and changes with network and attack environments, which leads to the poor performance and poor portability of intrusion detection algorithms. Therefore, this paper proposes an intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network. Firstly, a feature selection method based on symmetric uncertainty and approximate Markov blanket is proposed, which fully considers the correlation and redundancy of features, and also the correlation between combined features and the class label, so as to reduce the data dimensionality. Secondly, the CNN-LSTM classifier fused with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to extract the spatial features and temporal features to improve the classification performance. Finally, the Particle Swarm Optimization (PSO) algorithm is improved and used to automatically optimize the hyperparameters of the classifier, so that the classifier can be applied to different intrusion detection datasets with better generalization ability and portability. Experiments have verified the effectiveness and superiority of the proposed algorithm on multiple evaluation indicators. • An effective algorithm for intrusion detection is proposed in this paper. • Feature selection is based on symmetric uncertainty and approximate Markov blanket. • A fusion neural network is constructed to extract the spatial and temporal features. • The PSO algorithm is improved to automatically optimize the hyperparameters. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (d FDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the d FDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (d FDBARO) algorithm. Moreover, the practicability of the proposed d FDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed d FDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the d FDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO outperforms other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs. [ABSTRACT FROM AUTHOR]

Identifying students’ learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a student's learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students’ behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions. [ABSTRACT FROM AUTHOR]

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Lévy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer. • Q-learning is introduced to MPA for adaptively choosing search mode. • Quasi-opposition learning is used to enhance the exploration of MPA. • The proposed optimizer can achieve great performance for continuous optimization. [ABSTRACT FROM AUTHOR]

• The paper benefits from Reinforcement Learning in industrial control applications. • TD3 policy gradient is implemented to learn optimal PI controller dynamics. • The actor-network learns the optimal PI dynamics using the reward mechanism. • The performance of the optimal PI dynamics is compared with the other results. To benefit from the advantages of Reinforcement Learning (RL) in industrial control applications, RL methods can be used for optimal tuning of the classical controllers based on the simulation scenarios of operating conditions. In this study, the Twin Delay Deep Deterministic (TD3) policy gradient method, which is an effective actor-critic RL strategy, is implemented to learn optimal Proportional Integral (PI) controller dynamics from a Direct Current (DC) motor speed control simulation environment. For this purpose, the PI controller dynamics are introduced to the actor-network by using the PI-based observer states from the control simulation environment. A suitable Simulink simulation environment is adapted to perform the training process of the TD3 algorithm. The actor-network learns the optimal PI controller dynamics by using the reward mechanism that implements the minimization of the optimal control objective function. A setpoint filter is used to describe the desired setpoint response, and step disturbance signals with random amplitude are incorporated in the simulation environment to improve disturbance rejection control skills with the help of experience based learning in the designed control simulation environment. When the training task is completed, the optimal PI controller coefficients are obtained from the weight coefficients of the actor-network. The performance of the optimal PI dynamics, which were learned by using the TD3 algorithm and Deep Deterministic Policy Gradient algorithm, are compared. Moreover, control performance improvement of this RL based PI controller tuning method (RL-PI) is demonstrated relative to performances of both integer and fractional order PI controllers that were tuned by using several popular metaheuristic optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization and Differential Evolution. [ABSTRACT FROM AUTHOR]

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

Abstract: The conventional filtered-x least mean square (FxLMS) algorithm commonly employed for active noise control (ANC) is sensitive to disturbances acquired by the error microphone and yields poor performance in such scenario. To circumvent this problem, in this paper, a Wilcoxon FxLMS (WFxLMS) algorithm is proposed and used in the design of an efficient ANC which is robust to outliers in the secondary path and immune to burst noise acquired by the error microphone. It is demonstrated through simulation study that under such situation the proposed algorithm outperforms the traditional FxLMS algorithm. A particle swarm optimization (PSO) algorithm based robust ANC system, which does not require the modeling of the secondary path is also derived in the paper. Improved performance of the robust evolutionary ANC system over L2 norm based evolutionary ANC system is also shown. [Copyright &y& Elsevier]

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

Abstract: This paper proposes a self-adjusting e-course generation process, which support to provide a truly personalized learning environment. The proposed process is divided into four steps: (1) determining learning concept structure, (2) adjusting the difficulty of the e-learning material, (3) analyzing a learner’s ability and learning goals, and (4) composing personalized e-courses. Meanwhile, this paper applies the collaborative voting approach to determine the difficulty of the e-learning material, and the maximum likelihood estimation (MLE) to analyze a learner’s ability and her/his learning goals. Since evolutionary algorithms (EAs) have been developed to find close optimal solutions, this paper adopts them to compose personalized e-courses that meet individual learners’ demands. Once a learner learns one or more learning concepts covered in a personalized e-course, the feedback information from the learner must be returned to: (I) self-adjust the difficulty of the e-learning material for step 2, and (II) update the learners’ ability and learning goals for step 3. Furthermore, to find appropriate EAs for personalized e-course composition, this paper devises some experiments to compare two widely applied EAs, Genetic algorithms (GA) and Particle Swarm Optimization (PSO). When the number of e-learning materials is less than 300, the experimented results indicate that the executing effectiveness of PSO is better than that of GA. Besides, to validate the practicability of the proposed process, an e-course authoring tool based on the proposed process is developed to generate personalized e-courses. The generated personalized e-courses have been provided to 103 actual learners who participate in an “Introduction to Computer” curriculum. The investigation results indicate that the proposed process adapts to learners by utilizing the feedback from many learners. In other words, learning experiences of one organization/class can benefit to another organization/class’s learners in the same curriculum. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) is one of the most important research topics on swarm intelligence. Existing PSO techniques, however, still contain some significant disadvantages. In this paper, we present a new QBL-PSO algorithm that uses QBL (query-based learning) to improve both the exploratory and exploitable capabilities of PSO. Here, we apply a QBL method proposed in our previous research to PSO, and then test this new algorithm on a real case study on problems of power conservation. Our algorithm not only broadens the search diversity of PSO, but also improves its precision. Conventional PSO often snag on local solutions when performing queries, instead of finding better global solutions. To resolve this limitation, when particles converge in nature, we direct some of them into an “ambiguous solution space” defined by our algorithm. This paper introduces two ways to invoke this QBL algorithm. Our experimental results confirm that the proposed method attains better convergence to the global best solution. Finally, we present a new PSO model for solving multi-objective power conservation problems. Overall, this model successfully reduces power consumption, and to our knowledge, this paper represents the first attempt within the literature to apply the QBL concept to PSO. [Copyright &y& Elsevier]

Abstract: This paper presents an improved self-adaptive particle swarm optimization algorithm (ISAPSO) to solve hydrothermal scheduling (HS) problem. To overcome the premature convergence of particle swarm optimization (PSO), the evolution direction of each particle is redirected dynamically by adjusting the two sensitive parameters of PSO in the evolution process. Moreover, a new strategy is proposed to handle the various constraints of HS problem in this paper. The results solved by this proposed strategy can strictly satisfy the constraints of HS problem. Finally, the feasibility and effectiveness of proposed ISAPSO algorithm is validated by a test system containing four hydro plants and an equivalent thermal plant. The results demonstrate that the proposed ISAPSO can get a better solution in both robustness and accuracy while compared with the other methods reported in this literature. [Copyright &y& Elsevier]

Abstract: In this paper, we extend the classical economic order quantity model to allow for the demand rate influenced by both selling price and displayed stock level. A finite period system is considered under inflation for deteriorating items. In addition, we impose a ceiling on the number of on-display stocks. We also allow for shortages and general partial backlogging. The objective is to maximize the discounted total profit by finding the optimal replenishment number, replenishment schedule and selling price changed periodically. An effective search procedure is provided to find the optimal solution by employing the properties derived in this paper and particle swarm optimization algorithm. Several numerical examples are used to illustrate the features of the proposed model. [Copyright &y& Elsevier]

Abstract: This paper presents combination of differential evolution (DE) and biogeography-based optimization (BBO) algorithm to solve complex economic emission load dispatch (EELD) problems of thermal generators of power systems. Emission substances like NO X , SO X , COX, Power demand equality constraint and operating limit constraint are considered here. Differential evolution (DE) is one of the very fast and robust, accurate evolutionary algorithms for global optimization and solution of EELD problems. Biogeography-based optimization (BBO) is another new biogeography inspired algorithm. Biogeography deals with the geographical distribution of different biological species. This algorithm searches for the global optimum mainly through two steps: migration and mutation. In this paper combination of DE and BBO (DE/BBO) is proposed to accelerate the convergence speed of both the algorithm and to improve solution quality. To show the advantages of the proposed algorithm, it has been applied for solving multi-objective EELD problems in a 3-generator system with NO X and SO X emission, in a 6-generators system considering NO X emission, in a 6-generator system addressing both valve-point loading and NO X emission. The current proposal is found better in terms of quality of the compromising and individual solution obtained. [Copyright &y& Elsevier]

Abstract: This paper presents a chaotic self-adaptive particle swarm optimization algorithm (CSAPSO) to solve dynamic economic dispatch problem (DED) with value-point effects. The proposed algorithm takes PSO as the main evolution method. The velocity, a sensitive parameter of PSO, is adjusted dynamically to increase the precision of PSO. To overcome the drawback of premature in PSO, chaotic local search is imported into proposed algorithm. Moreover, a new strategy is proposed to handle the various constraints of DED problem in this paper, the results solved by proposed strategy can satisfy the constraints of DED problem well. Finally, the high feasibility and effectiveness of proposed CSAPSO algorithm is validated by three test systems consisting of 10 and extended 30 generators while compared with the experimental results calculated by the other methods reported in this literature. [Copyright &y& Elsevier]

Abstract: In this paper, a structural truss mass optimization on size and shape is performed taking into account frequency constraints. It is well-known that structural optimizations on shape and size are highly non-linear dynamic optimization problems since this mass reduction conflicts with the frequency constraints especially when they are lower bounded. Besides, vibration modes may switch easily due to shape modifications. This paper intends to investigate the use of a particle swarm optimization (PSO) algorithm as an optimization engine in this type of problem. This choice is based on reported well-behavior of such algorithm as global optimizer in other areas of knowledge. Another feature of the algorithm is taken into account for this choice, like the fact that it is not gradient based, but just based on simple objective function evaluation. The algorithm is briefly revised highlighting its most important features. It is presented four examples regarding the optimization of trusses on shape and size with frequency constraints. The examples are widely reported and used in the related literature as benchmarks. The results show that the algorithm performed similar to other methods and even better in some cases. [Copyright &y& Elsevier]

Abstract: This paper proposes modified particle swarm optimization to solve the problem of activity assignment of fourth party logistics (4PL) with preemptive structure. In practice, decision makers must consider goals of different importance when they encounter 4PL decision problems. Previous studies have adopted weighted fuzzy goal programming to design optimization problems. However, it is difficult for decision makers to determine proper weights. This paper proposes a decision making method based on preemptive fuzzy goal programming and a modified PSO. The proposed method does not require weights, and prevents results without feasible solutions caused by improper resource setting. Furthermore, this paper proposes a modified PSO with mutation operator extension. Numerical analysis shows that proposed modified PSOs prevent algorithms from caving prematurely into local optimums. [Copyright &y& Elsevier]

Abstract: This paper developed an optimized mathematical model to address the modification of spare parts. In addition, it can evaluate the execution of modified product plans; it also evaluates the suppliers and distributes spare parts supplied by the suppliers. In this mathematical model, analytic hierarchy process (AHP) was proposed for the formulation of factor weights. In addition, an improved particle swarm optimization (PSO) algorithm was also developed for solving the mathematical model; with the introduction of memory and inhibition mechanisms, it could eliminate worse results and excessive searches. Finally, this paper combines the improved mechanisms and four PSO speed-updating criterions. The case study shows that, this improved PSO has good solving capabilities. [Copyright &y& Elsevier]

In this paper, a novel iris feature extraction technique with intelligent classifier is proposed for high performance iris recognition. We use one dimensional circular profile to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1-D wavelet transform. So as to improve the accuracy, this paper combines probabilistic neural network (PNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. A comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The experimental results reveal the proposed algorithm provides superior performance in iris recognition. [Copyright &y& Elsevier]

Abstract: This paper proposes a self-generating fuzzy system with a learning ability from a combination of the on-line self-aligning clustering (OSAC) algorithm and ant and particle swarm cooperative optimization (APSCO). The proposed OSAC algorithm not only helps generate rules from on-line training data, but also helps avoid generating highly overlapping fuzzy sets. Once a new rule is generated, APSCO optimizes the corresponding antecedent and consequent parameters. In APSCO, ant colony and particle swarm coexist in a population, and both search for an optimal parameter solution simultaneously in each iteration. Ant paths not only help determine the consequent parameters of generated rules, they also help generate auxiliary particles. Well-performing particles are selected from the auxiliary particles and original particles. And these selected particles cooperate to find a better solution through particle swarm optimization. This paper applies the proposed self-generating fuzzy system to different fuzzy controller design problems, and compares it with other genetic and swarm intelligence algorithms and their hybrids to verify system performance. [Copyright &y& Elsevier]

• Automatic Artificial Intelligence Algorithm Selection. • Automatic Hyperparameter Optimization. • Particle Swarm Optimization which can handle continuous and discrete values. • Particle Swarm Optimization with Simulated Annealing acceptance criteria. • Global Optimal Solution. Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided; it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way. [ABSTRACT FROM AUTHOR]

In the area of association rule mining, many optimization algorithms have been proposed to improve the computational efficiency of rule mining or the quality and diversity of association rules. However, in real applications, since the user may have prior knowledge and research trends for some key items, the association rules containing key items are more valuable and meaningful for these users. This contributes to a new issue that association rules related to key items should be mined in a targeted manner. To solve this issue, this paper proposes a novel animal dynamic migration optimization (ADMO) method to realize directional rule mining as well as maintain high mining efficiency and high rule quality. Taking the support and confidence of frequent itemsets as input, the method first identifies valuable rules and then initializes and updates the animal population to search for the best animal. The support and confidence of the best animal are defined as threshold values to delete unnecessary rules and discover more key rules. During the optimization, the population size value is dynamically generated. The effectiveness and applicability of ADMO are validated on 11 open-source datasets and a real-world elevator case. The results reveal that the ADMO method has a faster mining speed and obtains more key rules than the ARM-PSO, ARM-AMO, ARM-MOPSO, ARM-WOA, and ARM-DE methods. In the elevator case, the association rule generated by ADMO can provide a higher success rate and accuracy for requirement transformation. • A heuristic optimization method is proposed to mine association rules directionally. • Valuable rules can be identified in advance to reduce computational consumption. • The population size value is dynamically generated without manually setting. • The proposed method has been tested on 11 real datasets and a real case. • The proposed method has been compared with the latest methods. [ABSTRACT FROM AUTHOR]

Target searching with autonomous robots require an efficient target search method that considers their constraints and environmental characteristics. Particle swarm optimization (PSO) is a fantastic population-based optimization algorithm. It is often used in swarm robotics cooperative search jobs because of its inspiration resources and velocity updating function. Given the global optimization features of PSO, it is simple to converge on a particular location in a search environment and miss out on opportunities to learn more. This paper proposes an adaptive exploration robotic PSO (AERPSO) to solve multi-target search problems. The proposed method enhances the chances of exploring unexplored regions and helps with obstacle avoidance using evolutionary speed and aggregation degree. The adaptive inertia weight helps in enhanced exploration. The simulation results compiled from various simulation experiments show that AERPSO performs way ahead of the existing state-of-the-art techniques for target searching. The proposed algorithm improves the search time by approximately 40% and the detection rate by 25% in comparison with existing approaches. It balances exploration and exploitation to become an excellent approach for multi-target searching. • To propose an adaptive exploration robotic PSO to search multiple targets. • The method avoids local minima and keeps the diversity among the population. • It enhances the exploration by adapting the inertia weights. • The proposed algorithm deals with overshooting problem of PSO. [ABSTRACT FROM AUTHOR]

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively. [ABSTRACT FROM AUTHOR]

• A new distance domination relation is proposed to select the non-dominant solution. • The association criterion of NSGA- III algorithm is improved. • Combined with the proposed distance domination relation improved NSGA- III algorithm. There are two main aspects of research in many-objective optimization algorithm, namely, convergence and diversity. However, it is difficult for original algorithms to maintain the diversity of solutions in the high-dimensional objective space. The NSGA-III algorithm is an advanced algorithm based on Pareto dominance. In the high-dimensional objective space, the diversity maintenance of this algorithm is obviously lacking. In order to enhance the diversity of algorithms in many-objective optimization problems, a new distance dominance relation is proposed in this paper. First, in order to ensure the convergence of the algorithm, the distance dominance relation calculates the distance from the candidate solution to the ideal point as the fitness value, and selects the candidate solution with good fitness value as the non-dominant solution. Then, in order to enhance the diversity of the algorithm, the distance dominance relation sets each candidate solution to have the same niche and ensures that only one optimal solution is retained in the same territory. Finally, the NSGA- III algorithm is improved based on the proposed distance dominance relation. On the DTLZ and MaF test problems with 3, 5, 8, 10, and 15 objectives, the improved algorithm is compared with seven commonly used algorithms. The experimental results show that the improved algorithm is highly competitive and can significantly enhance the diversity of the algorithm. [ABSTRACT FROM AUTHOR]

• A generalized decoupling metaheuristic RBDO framework is proposed. • A particle's memory saving strategy is developed to provide effective guidance. • A new adaptive fractional-order equilibrium optimizer algorithm is developed. • Numerical and engineering examples illustrate the superiority of the proposed algorithm. Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the ability of two new nature-inspired metaheuristics namely the flower pollination (FP) and the social spiders optimization (SSO) algorithms to solve the image segmentation problem via multilevel thresholding. The FP algorithm is inspired from the biological process of flower pollination. It relies on two basic mechanisms to generate new solutions. The first one is the global pollination modeled in terms of a Levy distribution while the second one is the local pollination that is based on random selection of local solutions. For its part, the SSO algorithm mimics different natural cooperative behaviors of a spider colony. It considers male and female search agents subject to different evolutionary operators. In the two proposed algorithms, candidate solutions are firstly generated using the image histogram. Then, they are evolved according to the dynamics of their corresponding operators. During the optimization process, solutions are evaluated using the between-class variance or Kapur's method. The performance of each of the two proposed approaches has been assessed using a variety of benchmark images and compared against two other nature inspired algorithms from the literature namely PSO and BAT algorithms. Results have been analyzed both qualitatively and quantitatively based on the fitness values of obtained best solutions and two popular performance measures namely PSNR and SSIM indices as well. Experimental results have shown that both SSO and FP algorithms outperform PSO and BAT algorithms while exhibiting equal performance for small numbers of thresholds. For large numbers of thresholds, it was observed that the performance of FP algorithm decreases as it is often trapped in local minima. In contrary, the SSO algorithm provides a good balance between exploration and exploitation and has shown to be the most efficient and the most stable for all images even with the increase of the threshold number. These promising results suggest that the SSO algorithm can be effectively considered as an attractive alternative for the multilevel image thresholding problem. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optimal because of its low global exploration efficiency; ABC algorithm has slower convergence speed in some cases because of the lack of powerful local exploitation capacity. In this paper, we propose a hybrid algorithm called PS–ABC, which combines the local search phase in PSO with two global search phases in ABC for the global optimum. In the iteration process, the algorithm examines the aging degree of pbest for each individual to decide which type of search phase (PSO phase, onlooker bee phase, and modified scout bee phase) to adopt. The proposed PS–ABC algorithm is validated on 13 high-dimensional benchmark functions from the IEEE-CEC 2014 competition problems, and it is compared with ABC, PSO, HPA, ABC–PS and OXDE algorithms. Results show that the PS–ABC algorithm is an efficient, fast converging and robust optimization method for solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

• In this paper, four new online portfolio selection algorithms have been proposed. • In these algorithms, clustering techniques are used in pattern matching approaches. • Transaction costs have been considered in the proposed algorithms. • Based on the results, the proposed algorithms outperform the competing algorithms. This paper presents an online portfolio selection algorithm based on pattern matching principle where it makes a decision on the optimal portfolio in each period and updates the optimal portfolio at the beginning of each period. The proposed method consists of two steps: i) sample selection, ii) portfolio optimization. First, in the sample selection, clustering algorithms including k-means, k-medoids, spectral and hierarchical clustering are applied to discover time windows (TW) similar to the recent time window. Then, after finding the similar time windows and predicting the market behavior of the next day, the optimum function along with the transaction cost is used in the portfolio optimization step in which, four algorithms including KMNLOG, KMDLOG, SPCLOG and HRCLOG are proposed for this purpose. The presented algorithms are applied on 5 different datasets with different characteristics including different markets, stocks, and time periods, and their performance has been evaluated. The results show that the provided algorithms in this paper, have better performance in terms of efficiency compared to the algorithms provided in the literature. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA. • The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization. • A greedy search and modified transition parameter are used to increase exploitation. • The multi-parent crossover is embedded to enhance diversity and global search. • The excellent performance of the ECr-SCA is validated on benchmark problems. • The ECr-SCA is also used to train multilayer perceptron. [ABSTRACT FROM AUTHOR]

• Hybridized Crow search algorithm (CSA) and Artificial Bee Colony (ABC). • Used three mechanisms to improve CSA and ABC known as FRCSA-ABC. • Tested on benchmark and Combined Heat and Economic Dispatch problems. • Validated its search capability and robustness in cost-effectiveness. • Conducted non-parametrical tests to prove its efficient optimization. This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual's best solution (p best) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence. [ABSTRACT FROM AUTHOR]

• Logarithmic spiral path enhances search agents' exploitation. • The exploration rate is made dynamic to improve the convergence. • The new procedure is tested by minimizing the 29 well known functions. • The proposed optimizer is validated using the 6 real cases. Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA's search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA's local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies' paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum. [ABSTRACT FROM AUTHOR]

• An improved muti-step initialization mechanism is presented. • Different decision variables adopted different operators to optimize particles. • A competition based learning strategy for updating swarm particles is suggested. • Experimental results show it achieved balance between convergence and diversity. Multi-objective particle swarm optimization (MOPSO) has been widely applied to solve multi-objective optimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ineffective in achieving the balance between convergence and diversity in the high-dimensional objective space. In this paper, an improved competitive particle swarm optimization is proposed for solving many-objective optimization problems. To improve the quality of the first generation population, a decision variable dividing-based multi-step initialization mechanism is presented, decision variables are divided into two groups and optimized individually. Moreover, an improved competitive learning strategy is suggested as the main part to further optimization, where particles are updated via leader information from winner particles with well convergence and diversity. The performance of the proposed algorithm is verified by benchmark comparisons with several state-of-the-art evolutionary algorithms. Experimental results demonstrate the promising performance of the algorithm in terms of balance convergence and diversity. [ABSTRACT FROM AUTHOR]

• FLAT algorithm was improved based on an enhanced version of BAT algorithm. • Proposed model integrates BAT with PSO using a novel infection propagation mechanism. • Performance of developed model was evaluated on a huge lengths protein sequence. • FLAT based on BPINF was used to detect longest consecutive subsequences of COVID19. • Performance of FLAT was evaluated using real dataset and compared with other methods. The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta -heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The scale-free network is an important type of complex network. The node degrees in a scale-free network follow the power-law distribution. In the skeleton of a scale-free network, there exists a few nodes which own huge neighborhood size and play an important role in information transmission of the entire network, while most of the network nodes have few connections whose influences of information exchange are limited to a relatively low level. In this paper, we introduce a scale-free population topology into the cuckoo search (CS) algorithm to propose a novel variant, which is termed the scale-free cuckoo search (SFCS) algorithm. Unlike other CS algorithms where the individuals exchange information randomly, two properties of the scale-free network can improve the SFCS algorithm in two aspects: the possibility that the information of competent individuals quickly floods the whole population is reduced significantly, which guarantees population diversity; and the corrupt individuals can learn from competent individuals with greater probability, which is beneficial for convergence. Thus, SFCS can obtain a better trade-off between exploitation and exploration during the search process. To evaluate the effectiveness of the proposed SFCS, 58 benchmark functions with different dimensions (10-D, 30-D, and 50-D), and 21 real-world optimization problems are employed in our experiment. We compare SFCS with the basic CS algorithm, two CS variants, and five state-of-the-art optimization algorithms, and the experimental results and statistical analysis verify the superiority of SFCS in terms of solution quality and convergence speed. Furthermore, we compare SFCS with a scale-free fully informed particle swarm optimization algorithm (SFIPSO) and the results prove our scale-free idea is effective despite its simplicity. We also introduce the scale-free population topology into the differential evolution (DE) and the firefly algorithm (FA) and the experimental results show that the scale-free population topology enhance the search ability of the DE and FA. These lead us to believe that the scale-free population topology may be a new technique for improving the performance of the population-based algorithms. • A modified cuckoo search algorithm with scale-free population topology (SFCS) is proposed. • Extensive performance comparison with other state-of-the-art algorithms is implemented. • The time complexity of the proposed algorithm is presented. • The performance of the algorithm on various real-world optimization problems is verified. [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: Parameter estimation for hydrological models is a challenging task, which has received significant attention by the scientific community. This paper presents a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization (MSSE-SPSO), which combines a particle swarm optimization with self-adaptive, hierarchical and multi-swarms shuffling evolution strategies. By comparison with particle swarm optimization (PSO) and a master–slave swarms shuffling evolution algorithm based on particle swarm optimization (MSSE-PSO), MSSE-SPSO is also applied to identify HIMS hydrological model to demonstrate the feasibility of calibrating hydrological model. The results show that MSSE-SPSO remarkably improves the calculation accuracy and is an effective approach to calibrate hydrological model. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) algorithm is an algorithmic technique for optimization by solving a wide range of optimization problems. This paper presents a new approach of extending PSO to solve optimization problems by using the feedback control mechanism (FCPSO). The proposed FCPSO consists of two major steps. First, by evaluating the fitness value of each particle, a simple particle evolutionary fitness function is designed to control parameters involving acceleration coefficient, refreshing gap, learning probabilities and number of the potential exemplars automatically. By such a simple particle evolutionary fitness function, each particle has its own search parameters in a search environment. Secondly, a local learning method using a competitive penalized method is developed to refine the solution. The FCPSO has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art algorithms, including traditional PSO algorithms and representative variants of PSO algorithms, the performance of FCPSO is promising. The effects of parameter adaptation, parameter sensitivity and local search method are studied. Lastly, the proposed FCPSO is applied to constructing a radial basis neural network, together with the K-means method for time-series prediction. [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: This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs). [Copyright &y& Elsevier]

Abstract: The minimum cross entropy thresholding (MCET) has been widely applied in image thresholding. The search mechanism of firefly algorithm inspired by the social behavior of the swarms of firefly and the phenomenon of bioluminescent communication, is used to search for multilevel thresholds for image segmentation in this paper. This new multilevel thresholding algorithm is called the firefly-based minimum cross entropy thresholding (FF-based MCET) algorithm. Four different methods that are the exhaustive search, the particle swarm optimization (PSO), the quantum particle swarm optimization (QPSO) and honey bee mating optimization (HBMO) methods are implemented for comparison with the results of the proposed method. The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5. The need of computation time of using the FF-based MCET algorithm is the least, meanwhile, the results using the FF-based MCET algorithm is superior to the ones of PSO-based and QPSO-based MCET algorithms but is not significantly different to the HBMO-based MCET algorithm. [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: In this paper a new hybrid method is proposed for multi-objective optimization problem. In multi-objective particle swarm optimization methods, selecting the global best particle for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions. Here, this problem is solved by incorporating charged system search method into the search process of the particle swarm optimization algorithm. In this approach, each particle is guided by its personal best and also resultant force which acted on this particle. This force is the consequence of the attraction field which is created around each archive member, where the magnitude of this force is related to the charge magnitude of the particles and also the distance between them. Each particle is guided by just archive members, which are located in the same region of the objective space as this particle. The proposed method is examined for different test functions and the results are compared to the results of three state-of-art multi-objective algorithms. [Copyright &y& Elsevier]

Abstract: Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu’s method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods. [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]