A Modified Spider Monkey Optimization Algorithm Based on GoodPoint Set and Enhancing Position Update

Spider monkey optimization (SMO), developed based on the behavior of spider monkeys, is a recently added swarm intelligence algorithm. SMO is a stochastic population-based metaheuristic algorithm. Spider monkeys have a fission-fusion social structure. In addition to being an excellent tool for solving optimization problems, SMO provides good exploration and exploitation capabilities. In this work, we present a modified strategy for improving the performance of the basic SMO in two ways: (a) we use the good-point-set method instead of random initial population generation; and (b) by changing both the local leader and global leader phases, we modify the SMO position update approach to increase global convergence while avoiding local optima. We evaluate the proposed modified SMO algorithm on 20 popular benchmark functions. The current findings prove that the proposed approach outperforms the standard SMO regarding result quality and convergence rate. © 2023 Slovene Society Informatika. All rights reserved.