A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model

Aoun, Oussama; Afia, Abdellatif El; Talbi, El‐Ghazali

doi:10.1007/978-3-030-58930-1_21

Cited by 2 publications

(1 citation statement)

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“…Regarding the first, [37] introduced the concept of multi-swarm optimization, which incorporates interaction mechanisms between the different swarms through a "master-slave" model. The same design has also been included in the cooperative multiswarm PSO proposed in [38]. In addition, [39] defined a cooperative PSO, with a different design: two surrogate assisted PSO variants (classical and social learning) cooperate to find the final (or a best) solution.…”

Section: Cooperative Learningmentioning

confidence: 99%

Chunking and cooperation in particle swarm optimization for feature selection

Sarhani

Voß

2021

Ann Math Artif Intell

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Bio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

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Section: Cooperative Learningmentioning

confidence: 99%

Chunking and cooperation in particle swarm optimization for feature selection

Sarhani

Voß

2021

Ann Math Artif Intell

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Deep Q-Network-Enhanced Self-Tuning Control of Particle Swarm Optimization

Aoun

2024

Modelling

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Particle Swarm Optimization (PSO) is a widespread evolutionary technique that has successfully solved diverse optimization problems across various application fields. However, when dealing with more complex optimization problems, PSO can suffer from premature convergence and may become stuck in local optima. The primary goal is accelerating convergence and preventing solutions from falling into these local optima. This paper introduces a new approach to address these shortcomings and improve overall performance: utilizing a reinforcement deep learning method to carry out online adjustments of parameters in a homogeneous Particle Swarm Optimization, where all particles exhibit identical search behaviors inspired by models of social influence among uniform individuals. The present method utilizes an online parameter control to analyze and adjust each primary PSO parameter, particularly the acceleration factors and the inertia weight. Initially, a partially observed Markov decision process model at the PSO level is used to model the online parameter adaptation. Subsequently, a Hidden Markov Model classification, combined with a Deep Q-Network, is implemented to create a novel Particle Swarm Optimization named DPQ-PSO, and its parameters are adjusted according to deep reinforcement learning. Experiments on different benchmark unimodal and multimodal functions demonstrate superior results over most state-of-the-art methods regarding solution accuracy and convergence speed.

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A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model

Cited by 2 publications

References 44 publications

Chunking and cooperation in particle swarm optimization for feature selection

Chunking and cooperation in particle swarm optimization for feature selection

Deep Q-Network-Enhanced Self-Tuning Control of Particle Swarm Optimization

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