A Neighborhood-Assisted Framework for Differential Evolution

Abstract

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 three-layer 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.

Keywords

• Differential evolution
• neighborhood information
• three-layer mechanism
• neighborhood selection strategy
• numerical optimization