Adaptive Differential Evolution based on Exploration and Exploitation Control

Abstract

Search operator design and parameter tuning are essential parts of algorithm design. However, they often involve trial-and-error and are very time-consuming. A new differential evolution (DE) algorithm with adaptive exploration and exploitation control (AEEC-DE) is proposed in this work to tackle this challenge. The proposed method improves the performance of DE by automatically selecting trial vector generation strategies (both mutation and crossover operators) and dynamically generating the associated control parameter values. A probability-based exploration and exploitation measurement is introduced to estimate whether the state of each newly generated individual is in exploration or exploitation. The state of historical individuals is used to assess the exploration and exploitation capabilities of different generation strategies and parameter values. Then, the strategies and parameters of DE are adapted following the common belief that evolutionary algorithms (EAs) should start with exploration and then gradually change into exploitation. The performance of AEEC-DE is evaluated through experimental studies on a set of test problems and compared with several state-of-the-art adaptive DE variants.