Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy for global optimization

Abstract

This study, incorporating three improvements into the structure of the original SSA algorithm to increase its exploration and exploitation capabilities, suggests an SSA variant algorithm named ”Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy (DSSA-R)”. Unlike the original SSA, DSSA-R accepts half of the population as the leader instead of using a single leader salp strategy. Nevertheless, the suggested algorithm includes two different equations to update the positions of the leader salps and follower salps. Furthermore, Refreshing Gap strategy was included in DSSA-R to avoid the algorithm from being stuck in the local optimum. The final improvement made on the original SSA was the addition of ”Linear Population Reduction Strategy” to strengthen the exploitation aspect of the algorithm. The performance of the DSSA-R algorithm was tested using the functions of the CEC 2014 and CEC 2015 benchmark suites and compared with five SSA variants and four state-of-the-art algorithms. Test results show that DSSA-R obtained better and competitive results compared to the algorithms it was compared with.