Cooperative Particle Swarm Optimization With a Bilevel Resource Allocation Mechanism for Large-Scale Dynamic Optimization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1000-1011
Number of pages12
Journal / PublicationIEEE Transactions on Cybernetics
Volume53
Issue number2
Online published17 Aug 2022
Publication statusPublished - Feb 2023

Abstract

Although cooperative coevolutionary algorithms are developed for large-scale dynamic optimization via subspace decomposition, they still face difficulties in reacting to environmental changes, in the presence of multiple peaks in the fitness functions and unevenness of subproblems. The resource allocation mechanisms among subproblems in the existing algorithms rely mainly on the fitness improvements already made but not potential ones. On the one hand, there is a lack of sufficient computing resources to achieve potential fitness improvements for some hard subproblems. On the other hand, the existing algorithms waste computing resources aiming to find most of the local optima of problems. In this article, we propose a cooperative particle swarm optimization algorithm to address these issues by introducing a bilevel balanceable resource allocation mechanism. A search strategy in the lower level is introduced to select some promising solutions from an archive based on solution diversity and quality to identify new peaks in every subproblem. A resource allocation strategy in the upper level is introduced to balance the coevolution of multiple subproblems by referring to their historical improvements and more computing resources are allocated for solving the subproblems that perform poorly but are expected to make great fitness improvements. Experimental results demonstrate that the proposed algorithm is competitive with the state-of-the-art algorithms in terms of objective function values and response efficiency with respect to environmental changes.

Research Area(s)

  • Optimization, Heuristic algorithms, Resource management, Statistics, Sociology, Particle swarm optimization, Dynamic scheduling, Balanced resource allocation, cooperative coevolution, large-scale dynamic optimization, particle swarm optimization (PSO), COEVOLUTION, FRAMEWORK, STRATEGY, OPTIMA