Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

Abstract

Differential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas.However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate thesedrawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutationand Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolutionof the population toward the global optimum. Furthermore, the effective particle encoding representation namedParticle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always producefeasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments wereconducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid compositionfunction, to validate performance of the proposed method and to compare with other state-of-the art DE variants suchas jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve differentrepresentative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate thatthe proposed method performs better for the majority of the single-objective scalable benchmark functions in terms ofthe solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Ganttchart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.