Improved Artificial Bee Colony Algorithm for Multimodal Optimization Based on Crowding Method

Many real-world problems can be transformed into multimodal functional optimization. Each of these problems may include several globally optimal solutions, rendering the solution of the problem progressively more difficult. In the study, the authors present a crowding artificial bee colony, called IABC, which exploits the concepts of crowding and explores search solutions. A crowding approach formed in niches is used to make it capable of tracking and maintaining multiple optima, resulting in good convergence of the search space with a better chance of locating multiple optima. Two new solution search mechanisms are proposed to increase population diversity and explore new search spaces. Experiments were carried out on 14 benchmark functions selected from previous literature. The results of the experiments show that the method is both effective and efficient. In terms of the quality of the success rate, the average number of optima found, and the maximum peak ratio, IABC performs better, or at least comparably, to other cutting-edge approaches.

Automated summary

The authors of this study propose a crowding artificial bee colony (IABC) algorithm that combines the concepts of crowding and search solution exploration for solving multimodal functional optimization problems. Experimental results demonstrate that the method is effective and efficient.