Hierarchical Design of Mechanical Metamaterials: an Application on Pentamode-like Structures

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110232 - 110232

Опубликована: Апрель 1, 2025

Язык: Английский

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Improved spherical search algorithm with memory-based dynamic population for optimization

The Journal of Supercomputing, Год журнала: 2025, Номер 81(5)

Опубликована: Апрель 10, 2025

Язык: Английский

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A probabilistic tournament learning swarm optimizer for large-scale optimization

Information Sciences, Год журнала: 2025, Номер unknown, С. 122189 - 122189

Опубликована: Апрель 1, 2025

Язык: Английский

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Auxiliary optimization framework based on scaling transformation matrix for large-scale multi-objective problem

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 95, С. 101931 - 101931

Опубликована: Апрель 11, 2025

Язык: Английский

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Test Case Optimization using Machine Learning based Hybrid Meta-Heuristic Approach

Journal of Electronic Testing, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

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Multi-task Ant Colony Optimization for Multi-vehicle Path Planning

Memetic Computing, Год журнала: 2025, Номер 17(2)

Опубликована: Май 26, 2025

Язык: Английский

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Gene-targeting multiplayer battle game optimizer for large-scale global optimization via cooperative coevolution

Cluster Computing, Год журнала: 2024, Номер unknown

Опубликована: Июнь 14, 2024

Язык: Английский

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

Modelling—International Open Access Journal of Modelling in Engineering Science, Год журнала: 2024, Номер 5(4), С. 1709 - 1728

Опубликована: Ноя. 15, 2024

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

Язык: Английский

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High-Dimensional Optimization using Diagonal and Low-Rank Evolution Strategy

Опубликована: Авг. 16, 2024

Язык: Английский

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A problem knowledge driven bi-population cooperative framework for time-varying ratio error estimation of voltage transformers

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 89, С. 101628 - 101628

Опубликована: Июнь 18, 2024

Язык: Английский

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