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Showing papers on "Crossover published in 2021"


Journal ArticleDOI
TL;DR: The analysis of recent advances in genetic algorithms is discussed and the well-known algorithms and their implementation are presented with their pros and cons with the aim of facilitating new researchers.
Abstract: In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

1,271 citations


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, which can improve PS distribution and convergence and maintain PF precision.

186 citations


Journal ArticleDOI
TL;DR: This in-depth research introduced horizontal crossover search and vertical crossover search into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time.
Abstract: The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based solver for realizing discrete problems. In order to make it also suitable for solving continuous problems, a variant of ACO (ACOR) has been proposed already. The deep-rooted ACO always stands out in the eyes of well-educated researchers as one of the best-designed metaheuristic ways for realizing the solutions to real-world problems. However, ACOR has some stochastic components that need to be further improved in terms of solution quality and convergence speed. Therefore, to effectively improve these aspects, this in-depth research introduced horizontal crossover search (HCS) and vertical crossover search (VCS) into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time. In CCACO, the HCS is mainly intended to increase the convergence rate. Meanwhile, the VCS and the developed selection mechanism are mainly aimed at effectively improving the ability to avoid dwindling into local optimal (LO) and the convergence accuracy. To reach next-level strong results for image segmentation and better illustrate its effectiveness, we conducted a series of comparative experiments with 30 benchmark functions from IEEE CEC 2014. In the experiment, we compared the developed CCACO with well-known conventional algorithms and advanced ones. All experimental results also show that its convergence speed and solution quality are superior to other algorithms, and its ability to avoid dropping into local optimum (LO) is more reliable than that of its peers. Furthermore, to further illustrate its enhanced performance, we applied it to image segmentation based on multi-threshold image segmentation (MTIS) method with a non-local means 2D histogram and Kapur's entropy. In the experiment, it was compared with existing competitive algorithms at low and high threshold levels. The experimental results show that the proposed CCACO achieves excellent segmentation results at both low and high threshold levels. For any help and guidance regarding this research, readers, and industry activists can refer to the background info at http://aliasgharheidari.com/ .

135 citations


Journal ArticleDOI
TL;DR: This study proposes a new Adaptive Polyploid Memetic Algorithm (APMA) for the problem of scheduling CDT trucks that can assist with proper CDT operations planning and substantially outperforms some of the state of the art metaheuristics with regards to solution quality and returns truck schedules that have lower total truck service cost.

103 citations


Journal ArticleDOI
TL;DR: The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixedknowledge transfer crossover operators.
Abstract: A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and solution quality. In MFEA, the knowledge transfer across tasks is realized via the crossover between solutions that possess different skill factors . This crossover is thus essential to the performance of MFEA. However, we note that the present MFEA and most of its existing variants only employ a single crossover for knowledge transfer, and fix it throughout the evolutionary search process. As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems. Nevertheless, to the best of our knowledge, there is no effort being conducted on the adaptive configuration of crossovers in MFEA for knowledge transfer, and this article thus presents an attempt to fill this gap. In particular, here, we first investigate how different types of crossover affect the knowledge transfer in MFEA on both single-objective (SO) and multiobjective (MO) continuous optimization problems. Furthermore, toward robust and efficient multitask optimization performance, we propose a new MFEA with adaptive knowledge transfer (MFEA-AKT), in which the crossover operator employed for knowledge transfer is self-adapted based on the information collected along the evolutionary search process. To verify the effectiveness of the proposed method, comprehensive empirical studies on both SO and MO multitask benchmarks have been conducted. The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixed knowledge transfer crossover operators.

95 citations


Journal ArticleDOI
TL;DR: In this paper, the authors employ a nature-mimicking optimization method, the genetic algorithm, in tandem with ML-based predictive models to design polymers that meet practically useful, but extreme, property criteria (i.e., glass transition temperature, T g > 500 K and bandgap, E g > 6 ǫ eV).

89 citations


Journal ArticleDOI
TL;DR: A novel approach applying the fast convergence speed of GD techniques of ANN and the global search capacity of EAs to train the network and proves that the proposed method is superior to traditional ANN, other hybrid-ANNs, and HGACs in terms of accuracy, and significantly reduces computational time compared with HGACS.

81 citations


Journal ArticleDOI
TL;DR: A multi-objective binary genetic algorithm integrating an adaptive operator selection mechanism (MOBGA-AOS) is proposed, which is capable of removing a large amount of features while ensuring a small classification error and is compared with five well-known evolutionary multi- objective algorithms on ten datasets.
Abstract: Feature selection is a key pre-processing technique for classification which aims at removing irrelevant or redundant features from a given dataset. Generally speaking, feature selection can be considered as a multi-objective optimization problem, i.e, removing number of features and improving the classification accuracy. Genetic algorithms (GAs) have been widely used for feature selection problems. The crossover operator, as an important technique to search for new solutions in GAs, has a strong impact on the final optimization results. However, many crossover operators are problem-dependent and have different search abilities. Thus, it is a challenge to select the most efficient one to solve different feature selection problems, especially when the nature of feature selection problems is unknown in advance. In order to overcome this challenge, in this paper, a multi-objective binary genetic algorithm integrating an adaptive operator selection mechanism (MOBGA-AOS) is proposed. In MOBGA-AOS, five crossover operators with different search characteristics are used. Each of them is assigned a probability based on the performance in the evolution process. In different phases of evolution, the proper crossover operator is selected by roulette wheel selection according to the probabilities to produce new solutions for the next generation. The proposed algorithm is compared with five well-known evolutionary multi-objective algorithms on ten datasets. The experimental results reveal that MOBGA-AOS is capable of removing a large amount of features while ensuring a small classification error. Moreover, it obtains prominent advantages on large-scale datasets, which demonstrates that MOBGA-AOS is competent to solve high-dimensional feature selection problems.

77 citations


Journal ArticleDOI
09 Apr 2021-Science
TL;DR: Results indicate that the gate-doped semiconductor provides an ideal platform for the two-dimensional BCS-BEC crossover without added complexities present in other solid-state systems.
Abstract: Bardeen-Cooper-Schrieffer (BCS) superfluidity and Bose-Einstein condensation (BEC) are the two extreme limits of the ground state of the paired fermion systems. We report crossover behavior from the BCS limit to the BEC limit realized by varying carrier density in a two-dimensional superconductor, electron-doped zirconium nitride chloride. The phase diagram, established by simultaneous measurements of resistivity and tunneling spectra under ionic gating, demonstrates a pseudogap phase in the low-doping regime. The ratio of the superconducting transition temperature and Fermi temperature in the low–carrier density limit is consistent with the theoretical upper bound expected in the BCS-BEC crossover regime. These results indicate that the gate-doped semiconductor provides an ideal platform for the two-dimensional BCS-BEC crossover without added complexities present in other solid-state systems.

71 citations


Journal ArticleDOI
TL;DR: An improved PSO with BSA called PSOBSA is proposed to resolve the original PSO algorithm's problems that BSA’s mutation and crossover operators were modified through the neighborhood to increase the convergence rate.
Abstract: The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor local optimum. Overcoming these defects is still a significant problem in PSO applications. In contrast, the backtracking search optimization algorithm (BSA) has a robust global exploration ability, whereas, it has a low local exploitation ability and converges slowly. This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm’s problems that BSA’s mutation and crossover operators were modified through the neighborhood to increase the convergence rate. In addition to that, a new mutation operator was introduced to improve the convergence accuracy and evade the local optimum. Several benchmark problems are used to test the performance and efficiency of the proposed PSOBSA. The experimental results show that PSOBSA outperforms other well-known metaheuristic algorithms and several state-of-the-art PSO variants in terms of global exploration ability and accuracy, and rate of convergence on almost all of the benchmark problems.

66 citations


Journal ArticleDOI
TL;DR: In this article, a multi-objective modeling of the network design problem is proposed to design accurate Convolutional Neural Networks (CNNs) with a small structure, which makes use of a graph-based representation of the solutions.
Abstract: With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

Journal ArticleDOI
TL;DR: This article is concerned with moving-horizon state estimation problems for a class of discrete-time linear dynamic networks, where signals are transmitted via noisy network channels and distortions can be caused by channel noises.
Abstract: This article is concerned with moving-horizon state estimation problems for a class of discrete-time linear dynamic networks. The signals are transmitted via noisy network channels and distortions can be caused by channel noises. As such, the binary encoding schemes, which take advantages of the robustness of the binary data, are exploited during the signal transmission. More specifically, under such schemes, the original signals are encoded into a bit string, transmitted via memoryless binary symmetric channels with certain crossover probabilities, and eventually restored by a decoder at the receiver. Novel centralized and decentralized moving-horizon estimators in the presence of the binary encoding schemes are constructed by solving the respective global and local least-square optimization problems. Sufficient conditions are obtained through intensive stochastic analysis to guarantee the stochastically ultimate boundedness of the estimation errors. A simulation example is presented to verify the effectiveness of the proposed moving-horizon estimators.

Journal ArticleDOI
TL;DR: In this paper, a color image cryptosystem based on improved genetic algorithm and matrix semi-tensor product (STP) is introduced, which is composed of five stages, preprocessing, DNA encoding, crossover, mutation and DNA decoding.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a particle swarm optimization-based feature selection method to solve high-dimensional classification via information sharing between two related tasks generated from a dataset, which can achieve higher classification accuracy with a smaller feature subset in a reasonable time than the state-of-the-art FS methods.
Abstract: Feature selection (FS) is an important data preprocessing technique in data mining and machine learning, which aims to select a small subset of information features to increase the performance and reduce the dimensionality. Particle swarm optimization (PSO) has been successfully applied to FS due to being efficient and easy to implement. However, most of the existing PSO-based FS methods face the problems of trapping into local optima and computationally expensive high-dimensional data. Multifactorial optimization (MFO), as an effective evolutionary multitasking paradigm, has been widely used for solving complex problems through implicit knowledge transfer between related tasks. Inspired by MFO, this study proposes a novel PSO-based FS method to solve high-dimensional classification via information sharing between two related tasks generated from a dataset. To be specific, two related tasks about the target concept are established by evaluating the importance of features. A new crossover operator, called assortative mating, is applied to share information between these two related tasks. In addition, two mechanisms, which are variable-range strategy and subset updating mechanism, are also developed to reduce the search space and maintain the diversity of the population, respectively. The results show that the proposed FS method can achieve higher classification accuracy with a smaller feature subset in a reasonable time than the state-of-the-art FS methods on the examined high-dimensional classification problems.

Journal ArticleDOI
TL;DR: A Modified Differential Evolution approach to Feature Selection (MDEFS) is proposed by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy.

Journal ArticleDOI
TL;DR: The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced.
Abstract: With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

Journal ArticleDOI
15 Jun 2021-Energy
TL;DR: A novel crisscross search based grey wolf optimizer (CS-GWO) is proposed, in which the hunting operation in GWO is modified by introducing a greedy mechanism and the horizontal crossover operator is added to refine the first three ranking wolves.

Journal ArticleDOI
TL;DR: A new uDE, Bezier Search Differential Evolution Algorithm, BeSD, has been proposed, which contains a partially elitist unique mutation operator and a unique crossover operator and is structurally simple, fast, unique and produce highly efficient trial patterns.
Abstract: Differential Evolution Algorithm (DE) is a commonly used stochastic search method for solving real-valued numerical optimization problems. Unfortunately, DE's problem solving success is very sensitive to the internal parameters of the artificial numerical genetic operators (i.e., mutation and crossover operators) used. Although several mutation and crossover methods have been developed for DE, there is not still an analytical method that can be used to select the most efficient mutation and crossover method while solving a problem with DE. Therefore, selection and parameter tuning processes of artificial numerical genetic operators used by DE are based on a trial-and-error process which is time consuming. The development of modern DE versions has been focused on developing fast, structurally simple and efficient genetic operators that are not sensitive to the initial values of their internal parameters. Problem solving successes of the Universal Differential Algorithms (uDE) are not sensitive to the structure and internal parameters of the related artificial numerical genetic operators used, unlike DE. In this paper a new uDE, Bezier Search Differential Evolution Algorithm, BeSD, has been proposed. BeSD’s mutation and crossover operators are structurally simple, fast, unique and produce highly efficient trial patterns. BeSD utilizes a partially elitist unique mutation operator and a unique crossover operator. In this paper, the experiments were performed by using the 30 benchmark problems of CEC2014 with Dim=30, and one 3D viewshed problem as a real world application. The problem solving success of BeSD was statistically compared with five top-methods of CEC2014, i.e., CRMLSP, MVO, WA, SHADE and LSHADE by using Wilcoxon Signed Rank test. Statistical results exposed that BeSD’s problem solving success is better than those of the comparison methods in general.

Journal ArticleDOI
TL;DR: A new rival genetic algorithm, as well as a fast version of rival Genetic algorithm, are proposed to enhance the performance of GA in feature selection to improve the global search capability.
Abstract: Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in feature selection tasks. However, GA suffers from the hyperparameter setting, high computational complexity, and the randomness of selection operation. Therefore, we propose a new rival genetic algorithm, as well as a fast version of rival genetic algorithm, to enhance the performance of GA in feature selection. The proposed approaches utilize the competition strategy that combines the new selection and crossover schemes, which aim to improve the global search capability. Moreover, a dynamic mutation rate is proposed to enhance the search behaviour of the algorithm in the mutation process. The proposed approaches are validated on 23 benchmark datasets collected from the UCI machine learning repository and Arizona State University. In comparison with other competitors, proposed approach can provide highly competing results and overtake other algorithms in feature selection.

Journal ArticleDOI
TL;DR: In this article, a predictive, diffusion-mediated coarsening model was proposed to explain crossover positions in Arabidopsis thaliana wild-type, an over-expressor of the conserved E3 ligase HEI10.
Abstract: In most organisms, the number and distribution of crossovers that occur during meiosis are tightly controlled. All chromosomes must receive at least one ‘obligatory crossover’ and crossovers are prevented from occurring near one another by ‘crossover interference’. However, the mechanistic basis of this phenomenon of crossover interference has remained mostly mysterious. Using quantitative super-resolution cytogenetics and mathematical modelling, we investigate crossover positioning in the Arabidopsis thaliana wild-type, an over-expressor of the conserved E3 ligase HEI10, and a hei10 heterozygous line. We show that crossover positions can be explained by a predictive, diffusion-mediated coarsening model, in which large, approximately evenly-spaced HEI10 foci grow at the expense of smaller, closely-spaced clusters. We propose this coarsening process explains many aspects of Arabidopsis crossover positioning, including crossover interference. Consistent with this model, we also demonstrate that crossover positioning can be predictably modified in vivo simply by altering HEI10 dosage, with higher and lower dosage leading to weaker and stronger crossover interference, respectively. As HEI10 is a conserved member of the RING finger protein family that functions in the interference-sensitive pathway for crossover formation, we anticipate that similar mechanisms may regulate crossover positioning in diverse eukaryotes. Crossover numbers and positions are tightly controlled but the mechanism involved is still obscure. Here, the authors, using quantitative super-resolution cytogenetics and mathematical modelling, show that diffusion mediated coarsening of HEI10, an E3-ligase domain containing protein, may explain meiotic crossover positioning in Arabidopsis.

Journal ArticleDOI
TL;DR: In this paper, a new discrete bat algorithm is proposed to solve the traveling salesman problem as NP-hard combinatorial optimization problem, where random walks based on Levy's flights are combined with bat's movement.
Abstract: Bat algorithm is a swarm-intelligence-based metaheuristic proposed in 2010. This algorithm was inspired by echolocation behavior of bats when searching their prey in nature. Since it first introduction, it continues to be used extensively until today, owing to its simplicity, easy handling and applicability to a wide range of problems. However, sometimes the major challenge faced by this technique is can be trapped in a local optimum when facing large complex problems. In this research work, a new discrete bat algorithm is proposed to solve the famous traveling salesman problem as NP-hard combinatorial optimization problem. To enhance the searching strategy and to avoid getting stuck in local minima, random walks based on Levy's flights are combined with bat’s movement. In addition, to improve the diversity and convergence of the swarm, a neutral crossover operator is embedded to the proposed algorithm. To evaluate the performance of our proposal, two experiments are conducted on 38 benchmark datasets and the obtained results are compared with eight different approaches. Furthermore, the student’s t-test, the Friedman’s test and the post hoc Wilcoxon's test are performed to check whether there are significant differences between the proposed optimizer and the alternative techniques. The experimental results under comparative studies have shown that, in most cases, the proposed discrete bat algorithm yields significantly better results compared with its competitors.

Journal ArticleDOI
TL;DR: An efficient and effective multi-objective method, namely DYN-MODPSO, is proposed, and where the traditional evolutionary clustering framework and the particle swarm algorithm are modified and enhanced, respectively.

Journal ArticleDOI
TL;DR: Experimental results revealed that the three modified algorithms provide competitive and superior performance in terms of finding optimal subset of molecular descriptors and maximizing classification accuracy compared to several well-established swarm intelligence algorithms including the original HHO.
Abstract: This paper presents modified versions of a recent swarm intelligence algorithm called Harris hawks optimization (HHO) via incorporating genetic operators (crossover and mutation CM) boosted by two strategies of (opposition-based learning and random opposition-based learning) to provide perfect balance between intensification and diversification and to explore efficiently the search space in order to jump out local optima. Three modified versions of HHO termed as HHOCM, OBLHHOCM and ROBLHHOCM enhance the exploitation ability of solutions and improve the diversity of the population. The core exploratory and exploitative processes of the modified versions are adapted for selecting the most important molecular descriptors ensuring high classification accuracy. The Wilcoxon rank sum test is conducted to assess the performance of the HHOCM and ROBLHHOCM algorithms. Two common datasets of chemical information are used in the evaluation process of HHOCM variants, namely Monoamine Oxidase and QSAR Biodegradation datasets. Experimental results revealed that the three modified algorithms provide competitive and superior performance in terms of finding optimal subset of molecular descriptors and maximizing classification accuracy compared to several well-established swarm intelligence algorithms including the original HHO, grey wolf optimizer, salp swarm algorithm, dragonfly algorithm, ant lion optimizer, grasshopper optimization algorithm and whale optimization algorithm.

Journal ArticleDOI
TL;DR: This article proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems that performs competitively against well-known evolutionary algorithms on the CEC’13 and CEC'17 test suites.
Abstract: Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. However, the determination process for the most suitable parameter setting is troublesome and time consuming. Adaptive control parameter methods that can adapt to problem landscape and optimization environment are more preferable than fixed parameter settings. This article proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems. In the approach, the parameter control is modeled as a finite-horizon Markov decision process. A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i.e., parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure. The differential evolution algorithm based on the learned agent is compared against nine well-known evolutionary algorithms on the CEC’13 and CEC’17 test suites. Experimental results show that the proposed algorithm performs competitively against these compared algorithms on the test suites.

Journal ArticleDOI
01 Oct 2021-Optik
TL;DR: A novel optimization method for wavefront-shaping focusing based on phase modulation that achieves a higher convergence rate and a larger enhancement than the sparrow search algorithm by introducing crossover and mutation operations.

Journal ArticleDOI
TL;DR: Experiments show that the proposed algorithm can achieve the best wirelength optimization and has a strong stability, especially for large-scale SMT problem, so as to better satisfy the demand of low delay of IC design under IEC architecture.

Journal ArticleDOI
TL;DR: In this article, an improved simulated annealing algorithm with crossover operator, called ISA-CO, was proposed to solve the capacitated vehicle routing problem (CVRP).
Abstract: The capacitated vehicle routing problem (CVRP) is one of the commonly studied issues today. It belongs to the class of NP-hard problems and has a high time complexity. Therefore, the solution of the CVRP was focused in this study. An improved simulated annealing algorithm with crossover operator, called ISA-CO, was proposed. A population based simulated annealing algorithm was used in the proposed algorithm. The solutions in the population were developed through the local search operators, including swap, scramble, insertion, and reversion. The improved 2-opt algorithm was used to develop the routes making up the solution. The partially mapped crossover (PMX) and the order crossover (OX) operators were applied to the solutions in the population to accelerate the convergence. A mix selection method was used to ensure the balance between exploitation and exploration. The ISA-CO was tested on 91 well-known benchmark instances. The results indicated that the method has a better performance compared to other state-of-the-art methods on many instances.

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the effect of various parameters on the hydrogen crossover rate in proton exchange membrane (PEM) electrolysers, such as the membrane temperature, hydrogen supersaturation, and compression of liquid/gas diffusion layer.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed an improved particle swarm optimization (IPSO) algorithm to obtain an optimal path for automated guided vehicles (AGVs) in a one-line production line in the workshop.
Abstract: In smart manufacturing workshops, automated guided vehicles (AGVs) are increasingly used to transport materials required for machine tools. This paper studies the AGV path planning problem of a one-line production line in the workshop, establishes a mathematical model with the shortest transportation time as the objective function, and proposes an improved particle swarm optimization(IPSO) algorithm to obtain an optimal path. In order to be suitable for solving the path planning problem, we propose a new coding method based on this algorithm, design a crossover operation to update the particle position, and adopt a mutation mechanism to avoid the algorithm from falling into the local optimum. By calculating the shortest transportation time obtained, the improved algorithm is compared with other intelligent optimization algorithms. The experimental results show that the algorithm can improve the efficiency of AGV in material transportation and verify the effectiveness of related improvement mechanisms.

Journal ArticleDOI
TL;DR: SFLBS has considerable accuracy in extracting the unknown parameters of the PV system problem, and its convergence speed is satisfactory, and SFLBS is used to evaluate three commercial PV modules under different irradiance and temperature conditions.