Comprehensive Survey of Hybrid Metaheuristic Algorithms

The action of combining the components from various algorithms is presently the utmost successful and effective trend in optimization. The primary goal of hybridizing disparate algorithmic concepts is to create better-performing systems that combine the advantages of many pure approaches–that is, hybrid systems that are meant to benefit from synergy. Actually, the secret to getting the best results while addressing a lot of challenging (complex) optimization issues is frequently to combine several algorithmic concepts in a proper way. However, developing a hybrid approach that is incredibly effective is not an easy undertaking. The hybridization of popular metaheuristics like Genetic algorithm, particle swarm optimization, evolutionary algorithms, simulated annealing, variable neighbourhood search, and ant colony optimization with techniques from other fields like artificial intelligence, operations research forms the basis of evolving more efficient and robust solutions. From the extensive review of the literature on hybrid Meta-heuristics algorithms in real life application, it is observed that their flexibility, effectiveness and robustness make them an increasing requirement for a variety of real-life optimization problems such as vehicle routing, traveling salesman problems, and supply chain network design, job scheduling.

The goal of this review paper is, to cover the present state of hybrid metaheuristics, summarising their progress, applications, and a wide range of opportunities for further research in a fast-evolving world. different type of hybridization is available: (i) Combinations of using metaheuristics with other metaheuristic methods; (ii) Hybridization of metaheuristics and precise optimization methodologies; (iii) Hybridization of metaheuristics and constraint programming methods; (iv) Integration of machine learning and data mining techniques into metaheuristic techniques.