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.
Growing client population, ever-increasing service demand, and complexity of services are the driving factors for the mobile operators for a paradigm shift in their core technology and radio access networks. 5G mobile network is the result of this paradigm shift and currently under deployment in many developed countries such as United States, United Kingdom, South Korea, Japan, and China-to name a few. However, most of the Least Developed Countries (LDCs) have very recently been implemented 4G mobile networks for which the overall role out phase is still not complete. In this paper, we investigate how feasible it is for LDCs to emphasize on a possible deployment of 5G networks at the moment. At first, we take a holistic approach to show the major technical challenges LDCs are likely to face while deploying the 5G mobile networks. Then we argue that various security aspects of 5G networks are an ongoing issue and LDCs are not technologically competent to handle many security glitches of 5G networks. At the same time, we show that most of the use cases of 5G networks are not applicable in the context of many LDCs (at least at the present time). Finally, this paper concludes that the start of the 5G network deployment in LDCs would take much longer time than expected.
The hybridization of meta-heuristics algorithms has achieved a remarkable improvement from the adaptation of dynamic parameterization. This paper proposes a variety of implementation frameworks for the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and the dynamic parameterization. In this paper, taxonomy of the PSO-GA with dynamic parameterization is presented to provide a common terminology and classification mechanisms. Based on the taxonomy, thirty implementation frameworks are possible to be adapted. Furthermore, different algorithms that used the implementation frameworks with sequential scheme and dynamic parameterizations approaches are tested in solving a facility layout problem.
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