A modified particle swarm optimization using adaptive strategy

Liu, Hao; Zhang, Xuewei; Tu, Liangping

doi:10.1016/j.eswa.2020.113353

Cited by 93 publications

(24 citation statements)

References 59 publications

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“…e particle swarm optimization (PSO) algorithm is a population-based random optimization method developed by Kennedy and Eberhart in 1995 [34], inspired by the social behavior of bird overcrowding and fish farming. Of course, this was just the beginning, and extensive research was conducted to improve this method, which led to stronger versions of this method provided by many authors [35][36][37] that the reader could see a summary of the development, improvement, and applications of this algorithm in [38]. To get a proper understanding of this method, consider a group of birds looking for food in an environment.…”

Section: The Related Work and The Classic Psomentioning

confidence: 99%

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Yousefikhoshbakht

2021

Complexity

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The traveling salesman problem (TSP) is one of the most important issues in combinatorial optimization problems that are used in many engineering sciences and has attracted the attention of many scientists and researchers. In this issue, a salesman starts to move from a desired node called warehouse and returns to the starting place after meeting n customers provided that each customer is only met once. The aim of this issue is to determine a cycle with a minimum cost for this salesman. One of the major weaknesses of the PSO algorithm in the classical version is that it gets stuck in local optimizations. Therefore, in the proposed algorithm, called MPSO, the best solution in the current iteration is also used in the movement step. In addition, a variety of local search algorithms are provided that are used when better answers are generated than before. Also, a new method for moving the particle towards the best particle is presented, which, in addition to probably increasing the quality of the new answer, prevents the premature convergence of the algorithm due to consideration of the concept of random. The results evaluated with the results of several metaheuristic algorithms in the literature show the efficiency of the MPSO algorithm because it has been able to achieve excellent solutions in most of these instances.

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Section: The Related Work and The Classic Psomentioning

confidence: 99%

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Yousefikhoshbakht

2021

Complexity

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“…The quality of initial population is one of the crucial factors that contribute to the quality of final solutions and optimization performance of PSO [33]. Conventionally, the velocity and positions of particles were randomly initialized using different statistical distributions such as those reported in [18], [27], [34]. In order to further accelerate the convergence speed of PSO, different initialization schemes were designed to achieve better and more uniform distributions of initial PSO population in solutions space.…”

Section: ) Modification In Population Initialization Schemesmentioning

confidence: 99%

Hovering Swarm Particle Swarm Optimization

2021

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PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.

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“…This MPSO was developed via a chaos-based nonlinear inertia weight. These researchers ran 30 benchmark functions to measure the convergence performance of MPSO in optimization problems [31].…”

Section: Swarm-based Algorithms and Chaosmentioning

confidence: 99%

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Atali¹,

Pehlivan²,

Gürevin³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area -besides engineering systems-where chaos theory is widely employed is optimization problems.Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already in process.In this article, scientific studies that cover the most popular metaheuristic algorithms in the literature in recent years and chaos theory subtopics together are examined. Great number of studies on metaheuristic algorithms and chaos issues exist in scientific literature. This article, hitherto, had to be limited to the most common meta-heuristic algorithms. In chaos-based metaheuristic algorithms, some advantages such as easy implementation, short application time and search acceleration have been addressed.This article is believed to contribute to two groups of researchers: The first group includes researchers already using meta-heuristic algorithms and who will come to understand that they can even improve their current techniques with chaos theory subarguments. The second group includes those who currently utilize chaotic analysis and methods in areas like nonlinear prediction modeling design and who will realize that they can make their existing methods even smarter with metaheuristic methods. Some metaheuristic algorithms use chaotic maps to solve problems such as trapping in local optimal solutions and premature convergence. In this study, such algorithms are examined through the benchmark functions they use. In addition, PSO, FA, ABC, WO algorithms are compared in terms of their common features, and algorithms with the best success rate are presented.

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A modified particle swarm optimization using adaptive strategy

Cited by 93 publications

References 59 publications

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Solving the Traveling Salesman Problem: A Modified Metaheuristic Algorithm

Hovering Swarm Particle Swarm Optimization

Chaos in metaheuristic based artificial intelligence algorithms: a short review

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