Shell Game Optimization: A Novel Game-Based Algorithm
Mohammad Dehghani, Zeinab Montazeri, O.P. Malik, Hadi Givi, Josep M. Guerrero
Abstract
This article presents a new game-based optimization method entitled Shell Game Optimization (SGO). The novelty of this article is simulating the rules of a game known as shell game to design an algorithm for solving optimization problems in different fields of science. The key idea of the SGO is to find the ball hidden under one of the three shells, which should be guessed by players. The main feature and advantage of SGO is that it does not have any control parameters and hence, there is no need to set parameters. SGO is mathematically modeled and implemented on 23 well-known benchmark test functions as well as on a real life-engineering problem entitled pressure vessel design problem. Moreover, SGO is compared with eight optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Spotted Hyena Optimizer (SHO), and Emperor Penguin Optimizer (EPO). The results and data obtained from applying SGO and other mentioned algorithms on unimodal test functions, multimodal test functions, and pressure vessel design problem show that SGO is able to provide better results in comparison with other well-known optimization algorithms. Moreover, results of Wilcoxon signed rank test confirm that SGO achieves more accuracy in comparison with the mentioned algorithms.