DGO: Dice Game Optimizer

Abstract

In recent years, optimization algorithms have been used in many applications. Most of these algorithms are inspired by physical processes or living beings' behaviors. This article suggests a new optimization method called “Dice Gaming Optimizer“ (DGO), which simulates dice gaming laws. This algorithm is inspired by an old game and the searchers are a set of players. Each player moves in the playground based on at least one and maximum six different players called guide’s players. The number of guide’s players for each player is determined by the number of dice. DGO is tested on 23 standard benchmark test functions and also compared with eight other algorithms such as: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS), Ant-Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm and Emperor Penguin Optimizer (EPO). Moreover, a real-life engineering design problem is solved by DGO. The results indicate that DGO have better performance as compared to the other well-known optimization algorithms.

Keywords

• optimization,metaheuristic algorithms,dice,dice game,Dice Game Optimizer,game

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