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Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimization
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AI
DOI：10.1007/s10489-024-05390-5.png)
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Abstract
En 中文
To effectively address large-scale optimization problems, this paper proposes an evolutionary dynamic grouping (EDG) based cooperative co-evolution (CC) algorithm. In the proposed algorithm, a novel decomposition method is designed to generate the sub-components of decision variables dynamically. Additionally, an evolutionary search method based on the fireworks search strategy is proposed to enhance the searchability of the algorithm. The performance of the proposed algorithm is assessed using two benchmark suites, IEEE CEC'2010 and IEEE CEC'2013, as well as a real-world optimization problem, the 0/1 Knapsack Problem (KP). Experimental results demonstrate that the proposed algorithm achieves competitive results when compared with other state-of-the-art algorithms.
Keywords:
Large-scale optimization
Cooperative co-evolution (CC)
Dynamic grouping
Fireworks search strategy
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