An adaptive encoding learning for artificial bee colony algorithms

Abstract

Recently, the improvements of artificial bee colony (ABCs) have attracted increasing interest in the studies of single optimization problems. However, most existing work of ABC aims to design new solution search equations and is still challenged when solving optimization problems with variable linkages. To overcome this limit, an adaptive encoding learning for ABCs (AEL+ABCs) is proposed in this paper. In AEL+ABCs, the solution search equations are encoded in both natural coordinate system and eigen coordinate system guided by covariance matrix learning. The purpose of the former is to maintain the diversity of population, while the latter aims at directing the evolution of population toward the promising directions by identifying the properties of fitness landscape. In addition, an adaptive selection mechanism is used to achieve a good tradeoff between convergence and diversity. For the comparison purposes, the proposed AEL strategy is applied to eight ABCs and their performance is tested on 30 CEC2014 benchmark functions. Experiment results show that the proposed AEL+ABCs can significantly improve the performance of the state-of-the-art ABCs in the majority of the benchmark functions.