Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

Yang, Qiang; Hua, Litao; Gao, Xudong; Xu, Dongdong; Lu, Zhenyu; Jeon, Sang-Woon; Zhang, Mengjie

doi:10.3390/math10050761

Cited by 31 publications

(13 citation statements)

References 77 publications

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“…To comprehensively verify the effectiveness of DELPSO, we compared it with several state-of-the-art PSO algorithms. Specifically, we selected nine representative and stateof-the-art PSO variants, namely XPSO [59], TCSPSO [61], DNSPSO [28], AWPSO [42], CLPSO_LS [48], HCLPSO [47], DPLPSO [62], SCDLPSO [6], and TLBO-FL [63]. Among these compared algorithms, XPSO, DNSPSO, AWPSO, DPLPSO, SCDLPSO, and TLBO-FL are topology-based PSO variants, while TCSPSO, CLPSO-LS and HCLPSO are constructive learning-based PSO variants.…”

Section: Methodsmentioning

confidence: 99%

“…These two limitations lead to the classical PSO preserving low search diversity but having fast convergence. Hence, it usually obtains promising performance on unimodal problems, but loses its effectiveness on multimodal problems [6,39].…”

Section: Related Work 21 Canonical Particle Swarm Optimizationmentioning

confidence: 99%

“…(rank(j)−1) 2 2×(0.2) 2 egs 2 (6) where rank(j) ∈ [1, egs] is the rank of the jth particle in EG after sorting particles in EG from the best to the worst.…”

Section: Differential Elite Learning Strategymentioning

confidence: 99%

“…In the classical PSO, each particle learns from its own historical best position and the global best position of the swarm discovered so far [1,5]. Such a learning strategy has two limitations [6,7]. On the one hand, the global best position is too greedy.…”

Section: Introductionmentioning

confidence: 99%

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Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

Yang

Gao

et al. 2022

Mathematics

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Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interacting variables and many wide and flat local basins. To alleviate this issue, this paper proposes a differential elite learning particle swarm optimization (DELPSO) by differentiating the two guiding exemplars as much as possible to direct the update of each particle. Specifically, in this optimizer, particles in the current swarm are divided into two groups, namely the elite group and non-elite group, based on their fitness. Then, particles in the non-elite group are updated by learning from those in the elite group, while particles in the elite group are not updated and directly enter the next generation. To comprise fast convergence and high diversity at the particle level, we let each particle in the non-elite group learn from two differential elites in the elite group. In this way, the learning effectiveness and the learning diversity of particles is expectedly improved to a large extent. To alleviate the sensitivity of the proposed DELPSO to the newly introduced parameters, dynamic adjustment strategies for parameters were further designed. With the above two main components, the proposed DELPSO is expected to compromise the search intensification and diversification well to explore and exploit the solution space properly to obtain promising performance. Extensive experiments conducted on the widely used CEC 2017 benchmark set with three different dimension sizes demonstrated that the proposed DELPSO achieves highly competitive or even much better performance than state-of-the-art PSO variants.

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Section: Methodsmentioning

confidence: 99%

Section: Related Work 21 Canonical Particle Swarm Optimizationmentioning

confidence: 99%

“…(rank(j)−1) 2 2×(0.2) 2 egs 2 (6) where rank(j) ∈ [1, egs] is the rank of the jth particle in EG after sorting particles in EG from the best to the worst.…”

Section: Differential Elite Learning Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

Yang

Gao

et al. 2022

Mathematics

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“…Paper [24] presented a stochastic cognitive superiority leading particle swarm optimization (SCDLPSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A new particle swarm optimization algorithm for identifying the hydraulic conductivity and diffusivity parameters in the unsaturated soil water

Wanga¹,

Panga²,

Suna³

et al. 2022

IJMSOR

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In this paper, we present the new particle swarm optimization algorithm by defining the inertia weight which depends on linearly the iteration step for solving the unsaturated soil water problem. By comparing with the exist particle swarm optimizations such as such as PSO algorithm, FPSO algorithm, P-PSO-SA algorithm and IDWPSO algorithm, numerical experiments verify that our proposed algorithm is more efficient and accurate to reach the optimal solution in the multimodal function extremum problem. Combining the characteristic difference method, our algorithm is applied to solve the unsaturated soil water problem. Numerical experiments show that our algorithm is quicker and more precision to approximate the exact solution. Keywords: Particle swarm optimization; Unsaturated soil water; Inertia weight; Characteristic difference; Parameter identification

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A random elite ensemble learning swarm optimizer for high-dimensional optimization

Yang

Song

Gao

et al. 2023

Complex Intell. Syst.

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High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder and harder to optimize due to increasingly interacting variables. To tackle such problems effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational learning theory. First, this optimizer partitions particles in the current swarm into two exclusive groups: the elite group consisting of the top best particles and the non-elite group containing the rest based on their fitness values. Next, it employs particles in the elite group to build random elite neighbors for each particle in the non-elite group to form a positive learning environment for the non-elite particle to observe. Subsequently, the non-elite particle is updated by cognitively learning from the best elite among the neighbors and collectively learning from all elites in the environment. For one thing, each non-elite particle is directed by superior ones, and thus the convergence of the swarm could be guaranteed. For another, the elite learning environment is randomly formed for each non-elite particle, and hence high swarm diversity could be maintained. Finally, this paper further devises a dynamic partition strategy to divide the swarm into the two groups dynamically during the evolution, so that the swarm gradually changes from exploring the immense solution space to exploiting the found optimal areas without serious diversity loss. With the above mechanisms, the devised REELSO is expected to explore the search space and exploit the found optimal areas properly. Abundant experiments on two popularly used high-dimensional benchmark sets prove that the devised optimizer performs competitively with or even significantly outperforms several state-of-the-art approaches designed for high-dimensional optimization.

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Stochastic Cognitive Dominance Leading Particle Swarm Optimization for Multimodal Problems

Cited by 31 publications

References 77 publications

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

A new particle swarm optimization algorithm for identifying the hydraulic conductivity and diffusivity parameters in the unsaturated soil water

A random elite ensemble learning swarm optimizer for high-dimensional optimization

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