Potential-Based Differential Evolution Algorithm With Joint Adaptation of Parameters and Strategies

Abstract

In the differential evolution (DE) algorithm, numerous studies have independently performed strategy adaptation and parameter adaptation. However, the strategy and parameters are interrelated in their impact on algorithm performance. It is well known that different problems and evolutionary stages require different appropriate parameters and strategies, but the fact that the same is true for different individuals is ignored. Few studies have focused on the difference in fitness values between two successive generations, which contains substantial evolution information. This study proposes a potential-based DE algorithm with joint adaptation of parameters and strategies (JAPSPDE). In JAPSPDE, a new population classification scheme, a new classification evolution mechanism, and a new joint adaptation mechanism are proposed to circumvent the three abovementioned issues. In the population classification scheme, individuals are divided into potential and unpotential individuals according to the improvement in fitness values between two generations. A classification evolution mechanism is applied by evolving potential individuals and unpotential individuals in two ways. In addition, a three-dimensional probability array is constructed to achieve joint adaptation of parameters and strategies. Finally, after properly combining the above algorithmic components, JAPSPDE can find the most appropriate combination of control parameters and mutation strategies for specific problems, stages, and individuals. The performance of JAPSPDE is evaluated in comparison with five well-known DE algorithms on BBOB2012 and CEC2014 and with six up-to-date evolution algorithms on CEC2014. The comparison results demonstrate the competitive performance of JAPSPDE.