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Ludo game-based metaheuristics for global and engineering optimization

2019; Elsevier BV; Volume: 84; Linguagem: Inglês

10.1016/j.asoc.2019.105723

1872-9681

Prabhat Ranjan Singh, Mohamed Abd Elaziz, Shengwu Xiong,

Evolutionary Algorithms and Applications

This paper proposes a Ludo game-based strategy to enhance the ability of swarm algorithms to solve numerous global optimization problems. The proposed strategy simulates the rules of playing the game Ludo using two or four players to perform the update process for different swarm intelligent behaviors. The proposed approach is named the Ludo Game-based Swarm Intelligence (LGSI) Algorithm. The LGSI algorithm uses the concepts of two and four players to enhance the exploration and exploitation of the optimization methods. In the proposed LGSI, a player is represented by a swarm algorithm, for example, in the two-player concept; Moth Flame Optimization (MFO) and the Grasshopper Optimization Algorithm (GOA) are selected, while in the four-player version, two other algorithms, the Sine Cosine Algorithm (SCA) and Gray Wolf Optimization (GWO), are added. In the proposed LGSI algorithm, the functional behaviors of all the used algorithms are different; also, there is no similarity among algorithmic behaviors except for convergence towards the global optimum, which is a common interest for all. However, the other algorithms share the same platform with this strategy, so in this case, competitive behavior may not be underestimated. The proposed LGSI algorithm shares positions among all the algorithms used during the search for the optimal solution. The performance of the LGSI algorithm is tested on a set of CEC2005 benchmark problems and engineering problems and is compared with the original versions of the utilized algorithms and a variety of other state-of-the-art algorithms. The experimental results show that the LGSI algorithm can provide promising and competitive results.