Elite-ordinary synergistic particle swarm optimization

Zhao, Shicun; Wang, Da

doi:10.1016/j.ins.2022.07.131

Cited by 10 publications

(2 citation statements)

References 38 publications

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“…The studies of improved PSO are mainly related to modified PSO algorithms and hybrid PSO algorithms based on meta-heuristic approaches [37]. Modified PSO algorithms are based on the updated model of PSO and adopted some strategies and methods in the search process of particles, such as flight mechanisms of particles including levy flight [38][39][40], learning strategies for particles including cirssoss learning [41], cognitive learning [42] and comprehensive learning [40]; population topology including stochastic topology [7] and dynamic topology [43]; and optimization strategies including random walk strategy [44], chaos strategy [45] and synergistic strategy [46]; search strategies including local search [47,48] and charged system search [49,50]. Hybrid PSO algorithms are combined with some traditional and evolutionary optimization methods in order to utilized the advantages of both methods and improve the global search ability of PSO, such as simulated annealing (SA) [51], tabu search (TS) [52], BBO [53], artificial bee colony (ABC) [54], genetic algorithm (GA) [55] and differential evolution (DE) [56].…”

Section: Related Workmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

Mathematics

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An extended membrane system with a dynamic nested membrane structure, which is integrated with the evolution-communication mechanism of a cell-like P system with evolutional symport/antiport rules and active membranes (ECP), and the evolutionary mechanisms of particle swarm optimization (PSO) and improved PSO inspired by starling flock behavior (SPSO), named DSPSO-ECP, is designed and developed to try to break application restrictions of P systems in this paper. The purpose of DSPSO-ECP is to enhance the performance of extended membrane system in solving optimization problems. In the proposed DSPSO-ECP, the updated model of velocity and position of standard PSO, as basic evolution rules, are adopted to evolve objects in elementary membranes. The modified updated model of the velocity of improved SPSO is used as local evolution rules to evolve objects in sub-membranes. A group of sub-membranes for elementary membranes are specially designed to avoid prematurity through membrane creation and dissolution rules with promoter/inhibitor. The exchange and sharing of information between different membranes are achieved by communication rules for objects based on evolutional symport rules of ECP. At last, computational results, which are made on numerical benchmark functions and classic test images, are discussed and analyzed to validate the efficiency of the proposed DSPSO-ECP.

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Section: Related Workmentioning

confidence: 99%

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

Mathematics

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“…In recent years, intelligent bionic algorithm has achieved very good results in solving the problem of optimization strategy. Many scholars are also constantly studying intelligent bionic algorithms, such as Ant Colony Algorithm [1], Fish Swarm Algorithm [2], Bee Colony Algorithm [3], Particle Swarm Optimization Algorithm [4], Wolf Swarm Algorithm [5], Cuckoo Algorithm [6], Harris Hawks Algorithm [7], etc. It also includes the bat algorithm proposed by Professor Yang in 2010 based on the ultrasonic obstacle avoidance and predator-prey characteristics of bats [8].…”

Section: Introductionmentioning

confidence: 99%

Improved Bat Algorithm Based on Fast Diving Strategy

Yanxiang Geng,

Liyi Zhang

et al. 2023

Journal of Internet Technology

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<p>Bat algorithm has good global search ability, but it has some problems, such as slow convergence speed in local search stage, low convergence accuracy, easy to fall into local optimization and can not escape. In view of the above defects, inspired by Harris Hawks’s strategy of catching rabbits, this paper introduces the surrounding mechanism of prey, which can quickly approach the food and judge its quality, so as to achieve the purpose of rapid convergence and improve the convergence accuracy. The experiment shows that the improved algorithm of the fast diving strategy is tested by using the test function, and compared with the basic bat algorithm, backtracking bat algorithm and HABC. The improved bat algorithm of the fast diving strategy has better optimization accuracy, faster convergence speed, simple algorithm and higher success rate.</p> <p> </p>

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Hierarchical learning particle swarm optimization using fuzzy logic

Wang

2023

Expert Systems with Applications

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Elite-ordinary synergistic particle swarm optimization

Cited by 10 publications

References 38 publications

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

An Extended Membrane System Based on Cell-like P Systems and Improved Particle Swarm Optimization for Image Segmentation

Improved Bat Algorithm Based on Fast Diving Strategy

Hierarchical learning particle swarm optimization using fuzzy logic

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