Blend of local and global variant of PSO in ABC

Tarun Kumar Sharma, Millie Pant, Ajith Abraham

Abstract: Artificial bee colony is a recently proposed metaheuristic optimization technique and is a new member of swarm intelligence based algorithms. It mimics the foraging behavior of honey bees. The performance of Artificial Bee Colony (ABC), like other metaheuristics, is heavily dependent on the tradeoff between their exploration and exploitation aptitude. In this paper a variant called Local Global variant Artificial Bee Colony (LGABC) is proposed to balance the exploration and exploitation in ABC. The proposal harnesses the local and global variant of Particle Swarm Optimization (PSO) into ABC. The proposed variant is investigated on a set of thirteen well known constrained benchmarks problems and three chemical engineering problems, which show that the variant can get high-quality solutions efficiently.