Selection Strategy and Opposition Based Learning for Global Optimization

The artificial bee colony (ABC) optimization algorithm has been widely used to solve the global optimization problems. Many versions of ABC algorithm exist in the literature intending to achieve optimum solution for problems in different domains. Some modifications of the ABC algorithm are general and can be applied to any problem domain, while some are application dependent. This paper proposes a modified version of the ABC algorithm named as, MABC-SS, that can be applied to any problem domain. The algorithm is modified in terms of population initialization and update of a bee position using the old and a new food source equation based on the algorithm’s performance in the previous iteration. The selection strategy is measured based on a novel approach called the rate of change. The population initialization in any optimization algorithm plays an important role in achieving the global optimum. The algorithm proposed in the paper uses random and an opposition-based learning technique to initialize the population and update a bee’s position after exceeding a certain number of trial limits. The rate of change is based on the average cost and is calculated for the past two iterations and compared for a method to be used in the current iteration to achieve the best result. The proposed algorithm is experimented with 35 benchmark test functions and 10 real world test functions. The findings indicate that the proposed algorithm is able to achieve the optimal result in most cases. The proposed algorithm is compared with the original ABC algorithm, modified versions of the ABC algorithm, and other algorithms in the literature using the test mentioned above. The results show that the proposed algorithm outperforms most algorithms and is comparable with some algorithms. The result is confirmed by Wilcoxon sum ranked test which shows the significance of the results