Novel enhanced Salp Swarm Algorithms using opposition-based learning schemes for global optimization problems

Salp Swarm Algorithm (SSA) is a recent approach with a simple implementation, few parameters, and low computational cost. SSA has been used in different optimization problems and provided competitive results compared to other popular metaheuristics. However, like other metaheuristics, SSA also faces problems such as lack of population diversity and premature convergence in local optima. Its other drawback is that it uses a random population initialization procedure that may produce solutions in a less effective starting point (i.e., initial population) with a poor diversity. In SSA, a single spatial search mode is used to update the position of followers, which makes it easy to fall in the local optima. SSA is more inclined to the exploitation phase, so it cannot always conduct a global search well and, in some cases, cannot find the global optimal solution. This work proposes an in-depth investigation of applying Opposition-based Learning (OBL) and its impact on the search space’s coverage, accuracy, exploration and exploitation, and convergence of the SSA’s search process. Hence, five enhanced hybrid SSA-OBL algorithms were developed and compared to the traditional SSA, Chimp Optimization Algorithm (ChOA), Giza Pyramids Construction (GPC), and Arithmetic Optimization Algorithm (AOA) in 15 IEEE CEC-2015 benchmark global minimization functions with 30, 50, and 100 dimensions. The Wilcoxon Signed Rank Test is used to test the statistical significance of the results. The proposed opposition-based SSAs statistically outperform basic SSA, ChOA, GPC, and AOA, respectively, with an average of 7.6, 15, 13.4, 15 out of 15 functions with 30 dimensions, 8, 15, 15, 15 with 50 dimensions, and 9.2, 15, 15, and 15 with 100 dimensions. Besides, we also applied the proposal in solving real-world optimization problems such as circular antenna array design problem and spacecraft trajectory optimization problem, and the opposition-based SSAs statistically outperform other competitive algorithms. The experiment results showed that incorporating different OBL schemes in SSA enhanced its exploration ability leading to better performance.