A multiple direction search algorithm for continuous optimization

Huang, W, He, J ORCID: https://orcid.org/0000-0002-5616-4691 and Zhu, L, 2025. A multiple direction search algorithm for continuous optimization. Swarm and Evolutionary Computation, 99: 102138. ISSN 2210-6502

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Abstract

The particle swarm optimization algorithm has been successfully applied to various optimization problems. One of its key features is the combination of particle velocity and search direction towards the optimal position in the history and swarm. Recognizing the limitations of the particle swarm optimization algorithm, this paper proposes a new evolutionary algorithm called the multiple direction search algorithm. The algorithm integrates five different search directions, including a multi-point direction constructed using principal component analysis. The integrated direction is generated by the weighted sum of the search directions. Theoretical analysis shows that under mild conditions, the rate of convergence along the weighted direction is no worse than the rate of convergence along the best of single search directions by a positive constant, or even faster in certain cases. The performance of the proposed algorithm was evaluated on three benchmark test suites by computer simulation. Experimental results demonstrate that the proposed method outperforms seven state-of-the-art particle swarm optimization algorithms.

Item Type:	Journal article
Publication Title:	Swarm and Evolutionary Computation
Creators:	Huang, W., He, J. and Zhu, L.
Publisher:	Elsevier BV
Date:	December 2025
Volume:	99
ISSN:	2210-6502
Identifiers:	Number Type 10.1016/j.swevo.2025.102138 DOI 2497149 Other
Rights:	© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	17 Sep 2025 10:50
Last Modified:	17 Sep 2025 10:50
URI:	https://irep.ntu.ac.uk/id/eprint/54349

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