A survey on new generation metaheuristic algorithms

By Tansel Dökeroğlu, Ender Sevinç, Tayfun Kucukyilmaz, Ahmet Cosar. Computers & industrial engineering, 2019

777 citations recorded by the source corpus.

Abstract Metaheuristics are an impressive area of research with extremely important improvements in the solution of intractable optimization problems. Major advances have been made since the first metaheuristic was proposed and numerous new algorithms are still being proposed every day. There is no doubt that the studies in this field will continue to develop in the near future. However, there is an obvious demand to pick out the best performing metaheuristics that are expected to be permanent. In this survey, we distinguish fourteen new and outstanding metaheuristics that have been introduced for the last twenty years (between 2000 and 2020) other than the classical ones such as genetic, particle swarm, and tabu search. The metaheuristics are selected due to their efficient performance, high number of citations, specific evolutionary operators, interesting interaction mechanisms between individuals, parameter tuning/handling concepts, and stagnation prevention methods. After giving absolute foundations of the new generation metaheuristics, recent research trends, hybrid metaheuristics, the lack of theoretical foundations, open problems, advances in parallel metaheuristics and new research opportunities are investigated.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/5ae28f80d35969fcfab356620a213e020eaf7b39

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In Artificial Intelligence

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In Industrial and Manufacturing Engineering

Citations in the corpus

A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “Metaheuristics can be divided into diﬀerent categories depending on their working philosophy and in-spiration [16, 17].”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, methodology): “When solving optimization problems, the parameters of metaheuristic algorithms have to be fine-tuned (Dokeroglu et al., 2019).”
Prairie Dog Optimization Algorithm (2022, methodology): “decades have witnessed a significant rise in the number of proposed nature-inspired optimization algorithms, and some imitate biological behaviour, physical phenomena, and many more [9].”