This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT ) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.
Differential evolution (DE), as a powerful and efficient evolutionary algorithm (EA), has shown its advantages in solving the complex optimization problems. In the literature, the utilization of neighborhood information has been attracting wide attention in the DE community due to its effectiveness in enhancing the search ability of DE. However, we have observed that no general framework is presented to provide a comprehensive way of studying the neighborhood-based DE variants. Therefore, this paper suggests a threelayer mechanism neighborhood-assisted (TLNA) DE framework to facilitate the utilization of neighborhood information. In TLNA, the mechanisms of using neighborhood information are generalized into a three-layer cooperative structure, i.e., the interaction mechanism (IM) layer, the organization mechanism (OM) layer, and utilization mechanism (UM) layer. Thus, TLNA is built to provide a synergistic effect of different layers of mechanisms for systematically utilizing neighborhood information. As a general framework, TLNA can be realized with different implementations of the three-layer mechanism. Furthermore, to demonstrate the practicality of the proposed framework, a TLNA instantiation (iTLNA) is given in detail. The performance of iTLNA is extensively evaluated on a suite of benchmark functions. The experimental results have confirmed the competitiveness of iTLNA to other DE variants and EAs, which shows that the proposed TLNA framework can pave an effective way to improve the performance of DE with neighborhood information.
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