Restart based Collective Information Powered Differential Evolution for Solving the 100-Digit Challenge on Single Objective Numerical Optimization

Abstract

Different from the classic differential evolution (DE) and many of its variants, collective information powered DE (CIPDE) is characterized by the utilization of collective information of population in the mutation and crossover processes of DE. This paper proposes a restart mechanism for CIPDE, to improve its robustness for solving the 100-digit challenge on single objective numerical optimization. Restart based CIPDE (rCIPDE) restarts the population when the unsuccessful update of the population exceeds a threshold value. Simulations on the challenge show that the restart mechanism enhances the performance on five out of the total 10 benchmark functions. According to the competition rule, rCIPDE achieves a total score of 85 (10 marks on each of F1-F7 and F10, 2 marks on F8 and 3 marks on F9).