Metaheuristic algorithms: A comprehensive review

Metaheuristic algorithms are computational intelligence paradigms especially used for sophisticated solving optimization problems. This chapter aims to review of all metaheuristics related issues. First, metaheuristic algorithms were divided according to metaphor based and non-metaphor based in order to differentiate between them in searching schemes and clarify how the metaphor based algorithms simulate the selected phenomenon behavior in the search area. The major algorithms in each metaphor subcategory are discussed including: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Water Waves Optimization (WWO), Clonal Selection Algorithm (CLONALG), Chemical Reaction Optimization (CRO), Harmony Search (HS), Sine Cosine Algorithm (SCA), Simulated Annealing (SA), Teaching-Learning-Based Optimization (TLBO), League Championship Algorithm (LCA), and others. Also, some non-metaphor based metaheuristics are explained as Tabu Search (TS), Variable Neighborhood Search (VNS). Second, different variants of metaheuristics are categorized into improved metaheuristics, adaptive, hybridized metaheuristics. Also, various examples are discussed. Third, a real-time case study “Welded Beam Design Problem” is solved with 10 different metaheuristics and the experimental results are statistically analyzed with non-parametric Friedman test in order to estimate the different performance of metaheuristics. Finally, limitation and new trends of metaheuristics are discussed. Besides, the chapter is accompanied with literature survey of existing metaheuristics with references for more details.