Selection scheme sensitivity for a hybrid Salp Swarm Algorithm: analysis and applications

This paper proposes a hybrid version of the Salp Swarm Algorithm (SSA) and the hill climbing (HC) technique using various selection schemes to solve engineering design problems. The proposed algorithm consists of two stages. In the first stage, the basic SSA is hybridized with HC local search to improve its exploitation capabilities; we refer to the hybridized algorithm as HSSA. In the second stage, a selection scheme is applied to enhance the exploration capabilities of the hybrid SSA. Six popular selection schemes were investigated, and the proportional selection scheme was selected as it yielded the best performance. We refer to the hybridized SSA along with the proportional selection scheme as PHSSA. To validate the performance of the proposed algorithms, a series of experiments were conducted using thirty benchmark functions and four engineering design problems. The investigations using benchmark functions revealed that HSSA overcame the weaknesses of the local search in the basic SSA. Moreover, PHSSA enhanced performance by providing an appropriate balance between exploration and exploitation as well as maintaining the diversity of the solutions and avoiding premature convergence. Finally, PHSSA produced results on engineering design problems that were at least comparable and in many cases superior to SSA and similar algorithms in the literature.

Automated summary

This paper proposes an improved version of the Salp Swarm Algorithm (SSA) for solving engineering design problems by combining it with the hill climbing technique. Experimental results demonstrate the superior performance of the proposed algorithm compared to existing algorithms.