Harmonized salp chain-built optimization

As an optimization paradigm, Salp Swarm Algorithm (SSA) outperforms various population-based optimizers in the perspective of the accuracy of obtained solutions and convergence rate. However, SSA gets stuck into sub-optimal solutions and degrades accuracy while solving the complex optimization problems. To relieve these shortcomings, a modified version of the SSA is proposed in the present work, which tries to establish a more stable equilibrium between the exploration and exploitation cores. This method utilizes two different strategies called opposition-based learning and levy-flight (LVF) search. The algorithm is named m-SSA, and its validation is performed on a well-known set of 23 classical benchmark problems. To observe the strength of the proposed method on the scalability of the test problems, the dimension of these problems is varied from 50 to 1000. Furthermore, the proposed m-SSA is also used to solve some real engineering optimization problems. The analysis of results through various statistical measures, convergence rate, and statistical analysis ensures the effectiveness of the proposed strategies integrated with the m-SSA. The comparison of the m-SSA with the conventional SSA, variants of SSA and some other state-of-the-art algorithms illustrate its enhanced search efficiency.