Parameter Combination Framework for the Differential Evolution Algorithm

Abstract

The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.

A Decomposition and Dynamic Programming Aggregation Method for the Optimal Water Allocation of Reservoirs in Series

“…Nevertheless, the meta-heuristic algorithms may fail to handle models with some complicated constraints. What is worse, the algorithm parameters, such as the crossover and mutation rates in GA and the inertia weight and acceleration coefficients in PSO, cannot be clearly determined despite the fact that these values are important factors which can affect the performances of algorithms (Zhang and Dong 2019).…”