A new selection operator for differential evolution algorithm

Abstract

Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.