Multi-Layer Competitive-Cooperative Framework for Performance Enhancement of Differential Evolution
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Differential Evolution (DE) is one of the most powerful optimizers in the evolutionary algorithm (EA) family. In recent years, many DE variants have been proposed to enhance performance. However, when compared with each other, significant differences in performances are seldomly observed. To meet this challenge of a more significant improvement, this paper proposes a multi-layer competitive-cooperative (MLCC) framework to combine the advantages of multiple DEs. Existing multi-method strategies commonly use a multi-population based structure, which classifies the entire population into several subpopulations and evolve individuals only in their corresponding subgroups. MLCC proposes to implement a parallel structure with the entire population simultaneously monitored by multiple DEs assigned in multiple layers. Each individual can store, utilize and update its evolution information in different layers by using a novel individual preference based layer selecting (IPLS) mechanism and a computational resource allocation bias (RAB) mechanism. In IPLS, individuals only connect to one favorite layer. While in RAB, high quality solutions are evolved by considering all the layers. In this way, the multiple layers work in a competitive and cooperative manner. The proposed MLCC framework has been implemented on several highly competitive DEs. Experimental studies show that MLCC variants significantly outperform the baseline DEs as well as several state-of-the-art and up-to-date DEs on the CEC benchmark functions.
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