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.
Highlights
Differential evolution (DE) is a simple and efficient evolutionary algorithm that is used primarily for real-parameter global optimization [1]
Parameters of population-based intelligent optimization algorithms can be divided into three categories: (1) operation-related parameters such as scale factor (F) and crossover probability (CR), which are used by search operations in classical DE; (2) population size (NP) and related parameters generated by strategies for dynamically adjusting the population; and (3) high-level strategy parameters: some algorithms use multi-operation, multi-strategy, or multi-population mechanisms, and may introduce new parameters
We proposed an idea as parameter region division and parameter strategy combination
Summary
Differential evolution (DE) is a simple and efficient evolutionary algorithm that is used primarily for real-parameter global optimization [1]. It has been successfully applied to many real-world problems [2,3,4,5]; the performance of DE algorithms greatly depends on parameter settings, especially in complex optimization problems. It is important to study parameter control strategies for DE algorithms. A number of parameter strategies have been proposed [6,7,8]. The second category of parameters has been gradually paid attention to. The third category of parameters is related to specific high-level strategies and has no universal significance
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