Ship Rescue Optimization: A New Metaheuristic Algorithm for Solving Engineering Problems

Abstract

In recent years, more and more problems in the industry have started to be solved using metaheuristics. In this paper, inspired by the ship maneuvering motion function and the rescue process, we propose ship rescue optimization (SRO) to solve the challenging optimization problem. The ship rescue process can be divided into two types of delayed rescue (large area rescue) and immediate rescue (small rescue) according to the searched person, and we can correspond these two types of rescue behaviors to the search space exploration and exploitation processes, respectively. In this process, SRO simulates the motion process of ship rescue according to the ship maneuvering equation of motion and finally comes up with an optimal position update algorithm. We verified the performance of the proposed algorithm on different dimensions of 57 test functions consisting of CEC2013 and CEC2017 and on three real engineering problems, and compared it with eight current mainstream algorithms. The algorithm proposed in this paper is shown to be robustly applicable in solving challenging optimization problems.