A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions

Abstract

The Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used successfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm.