Efficient Artificial Bee Colony Optimization

Abstract

Artificial bee colony (ABC) algorithm is one of the proficient meta-heuristic technique in the field of nature inspired algorithms to solve the optimization problems. ABC has been proven itself as better candidate in the field of nature inspired algorithms. But, still it shows some limitations like improper balance betwixt exploration and exploitation, premature convergence and stagnation problem. To overcome these limitations, a new variant of ABC algorithm named as Efficient Artificial Bee Colony Optimization (EABC) algorithm. In the proposed EABC, three new strategies are incorporated named as Self-Adaptive Strategy, Self-Adaptive Mutual Learning Strategy, and Exploring Strategy. The Self-Adaptive Strategy is incorporated in the employed bee phase and it help to improve the balance betwixt exploration and exploitation. The Self-Adaptive Mutual Learning Strategy is applied on onlooker phase and it help remove premature convergence. And last Exploring Strategy is applied on scout bee phase to remove stagnation and improve the optimal searching ability. The EABC is compared over 21 test benchmark functions and there results are compared with basic version of ABC, its significant variants namely, Best So Far ABC (BSFABC), Modified ABC (MABC), Black Hole ABC (BHABC), Memetic ABC (MeABC) and one recent swarm intelligence based Spider Monkey Optimization (SMO). The examination of the outcomes demonstrates that the proposed EABC Algorithm is a competitive variant of ABC.