Abstract
Real-world optimization problems need methods and techniques to explore search space and avoid local optima in order to find the best solutions. Salp Swarm Algorithm (SSA) is a novel algorithm which mimics the behavior of Salps during foraging and navigation in the ocean. Unfortunately, like other similar algorithms, SSA may fail in avoiding local optima and have a slow convergence curve. In this work, a novel version of SSA named OBSSA is introduced which is depends on Opposition-Based Learning (OBL) Strategy. The proposed algorithm consists of 2 stages: in the first stage where OBL is used to enhance the initialization stage and in the second stage where OBL is used in the updating process of the population in every iteration. To investigate the performance of OBSSA, 30 functions from IEEE Congress on Evolutionary Computation (IEEE CEC2014) and 6 engineering problems are tested and compared with 8 state-of-art algorithms. Moreover, non-parametric test Wilcoxon’s rank-sum is performed at 5% level to test the significant of the obtained results between OBSSA and SSA. The results show that OBSSA can get competitive results with other algorithms.
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