Work

Neuro-PSO algorithm for large-scale dynamic optimization

Year: 2025

Type: article

Abstract: Over the last few decades, dynamic optimization and large-scale optimization have been two challenging research topics. In this context, dynamic optimization with high dimensionality is undoubtedly another important research topic. For such a combined problem, this paper develops: (1) an algorithm that incorporates problem decomposition to deal with high dimensionality, (2) a search algorithm for optimization, and (3) a prediction strategy to deal with dynamic changes. Firstly, a decomposition method is introduced to divide the problem into multiple subproblems based on the level of interactions among the decision variables. For optimization, a multi-population search algorithm is proposed, where each subpopulation evolves individually. Finally, a machine learning-based prediction strategy is developed to learn information from historical solutions and predict some solutions that may be useful for the new environment. The proposed algorithm is tested using the generalized moving peaks benchmark problems. The results show that the proposed algorithm can find better solutions than existing approaches.

Source: Swarm and Evolutionary Computation

DOI: 10.1016/j.swevo.2025.101865

Authors: Mohamed Radwan, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos Coello Coello

Institutions: UNSW Sydney, UNSW Canberra, University of Sharjah, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Tecnológico de Monterrey

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,690

Funders: Consejo Nacional de Ciencia y Tecnología, Consejo Nacional de Ciencia y Tecnología, Paraguay, Australian Research Council