Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances

Abstract

Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

“…For each problem, an objective function is established to compute the fitness values of solutions. Defining the suitable objective function for each problem is one of the essential steps in optimization [ 135 , 136 ]. Depending on the problem's nature and the objective function, optimization problems are generally categorized into three main categories: single-objective, multi-objective, and many-objective.…”

Dingo‐optimization‐based task‐offloading algorithm in multihop V2V/V2I‐enabled networks

“…This paper describes the multihop V2V/V2I offloading problem as an NP‐hard problem. Since no specific techniques exist for producing solutions in polynomial time, metaheuristic algorithms are widely used in optimization problems as an effective approach to tackle complex challenges 25 . Compared with other metaheuristic algorithms for solving discrete problems, DDOA has a good convergence speed and solution quality.…”

“…Since no specific techniques exist for producing solutions in polynomial time, metaheuristic algorithms are widely used in optimization problems as Step4:Determine the hunting strategy: Calculate the optimal solution of the new population Fitness(dingox new ); iteration = iteration + 1 24: end while 25: Display dingox * , the best optimal solution an effective approach to tackle complex challenges. 25 Compared with other metaheuristic algorithms for solving discrete problems, DDOA has a good convergence speed and solution quality. This feature can complete the task offloading in time and thus well solve the low latency requirements in IoV scenarios.…”

Localisation of facts compensator using reptile search algorithm for enhancing power system security under multi‐contingency conditions

“…A new meta‐heuristic optimisation technique inspired by nature has been devised to overcome the aforementioned problems. The naturally inspired algorithms contain a success rate mostly owing to their flexibility and ability to solve NP‐hard problems, so it is often used for non‐linear problems solving engineering applications [15]. The RSA, a new meta‐heuristic update motivated through crocodiles' hunting behaviour, is included in the proposed model.…”