Artificial Bee Colony Optimization Algorithm Based on Adaptive Evolution Strategy

Abstract

An artificial bee colony optimization algorithm based on adaptive evolution strategy is proposed to improve the performance of the artificial bee colony algorithm. Each leader individual has four evolutionary strategies in the algorithm. In the iteration process, the evolutionary behavior of the leader individual is determined by calculating the immediate value, the future value and the comprehensive reward of each evolutionary strategy. And then a multi-strategy evolutionary probability mutation method is proposed to improve the individual search speed or to avoid falling into the local optimal solution. Typical high-dimensional complex function tests show that the algorithm has good convergence accuracy and computational speed.