Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

Ma, Zhiteng; Yuan, Xianfeng; Han, Song; Sun, Deyu; Ma, Yan

doi:10.3390/sym11070876

Cited by 32 publications

(18 citation statements)

References 31 publications

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“…Another promising research area is chaos theory in evolutionary optimization. The initial locations of agents in swarm intelligence algorithms affect the quality of the obtained solution, so a chaos-based pseudorandom number generator could be used to initialize the population in an evolutionary-inspired algorithm [50].…”

Section: Discussionmentioning

confidence: 99%

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Demidova

Gorchakov

2020

Algorithms

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Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.

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Section: Discussionmentioning

confidence: 99%

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Demidova

Gorchakov

2020

Algorithms

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“…Fan and Jen attempted to develop the basic PSO effectiveness and convergence by adopting the thought of multiple cooperative swarms, and then, an enhanced partial search PSO (EPS-PSO) [29] was proposed. Ma et al proposed the chaotic PSO-arctangent acceleration coefficient algorithm (CPSO-AT) [30], which strengthened the effectiveness of the original PSO through chaotic initialization and nonlinear optimization. Zhang et al proposed a cooperative coevolutionary bare-bones particle swarm optimization (CCBBPSO) with function independent decomposition (FID), called CCBBPSO-FID [31].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

2021

Mobile Information Systems

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Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

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“…Finding a balance between the two, i.e., global search and local search, is a challenging task. Ma [19] proposed a chaotic PSO algorithm with arctangent acceleration coefficient to seek a balance between global search and local search. Wang [20] proposed a hybrid quantum PSO algorithm, which uses flight and jump operations to improve the accuracy of QPSO (Quantum Particle Swarm Optimization) and enhance the search ability.…”

Section: Introductionmentioning

confidence: 99%

Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement

Qin

2020

Symmetry

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In this paper, an improved PSO (Particle Swarm Optimization) algorithm is proposed and applied to the infrared image enhancement. The contrast of infrared image is enhanced while the image details are preserved. A new exponential center symmetry inertia weight function is constructed and the local optimal solution jumping mechanism is introduced to make the algorithm consider both global search and local search. A new image enhancement method is proposed based on the advantages of bi-histogram equalization algorithm and dual-domain image decomposition algorithm. The fitness function is constructed by using five kinds of image quality evaluation factors, and the parameters are optimized by the proposed PSO algorithm, so that the parameters are determined to enhance the image. Experiments showed that the proposed PSO algorithm has good performance, and the proposed image enhancement method can not only improve the contrast of the image, but also preserve the details of the image, which has a good visual effect.

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Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

Cited by 32 publications

References 31 publications

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement

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