Abstract
Particle swarm optimization (PSO) does not scale well to large-scale optimization problems (LSOPs). A divide-and-conquer approach towards solving LSOPs has been shown to be very effective in scaling PSO, resulting in a family of co-operative PSO (CPSO) algorithms. Recently, two adaptive co-operative PSO approaches have been developed to improve performance on non-separable problems, namely decomposition CPSO (DCPSO) and merging CPSO (MCPSO). Though DCPSO and MCPSO were shown to perform competitively, they are limited in their ability to explore variable groupings. This paper proposes incorporating random grouping of decision variables into DCPSO (RG-DCPSO) and MCPSO (RG-MCPSO) to better cope with complex variable dependencies. These algorithms were compared to results from five other decomposition-based approaches in order to determine if applying random grouping to DCPSO and MCPSO leads to an improvement in performance. The empirical results show that when applied to function optimization problems, RG-DCPSO was able to achieve the best overall final objective function values in environments with up to 1000 dimensions. The results also show that RG-MCPSO performs well for non-separable objective functions in large-dimensional spaces with 500 and 1000 dimensions.
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Notes
- 1.
\(n_x\) is the number of decision variables.
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McNulty, A., Ombuki-Berman, B., Engelbrecht, A. (2022). Decomposition and Merging Co-operative Particle Swarm Optimization with Random Grouping. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_10
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