An Improved Artificial Bee Colony Algorithm for Job Shop Problem

Yao, Bao Zhen; Yang, Chui‐Ping; Hu, Juan; Yin, Guo; Yu, Bo

doi:10.4028/www.scientific.net/amm.26-28.657

Cited by 18 publications

(2 citation statements)

References 12 publications

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“…Researchers have implemented several strategies to enhance the performance of ABC. Yao et al [77] developed an Improved Artificial Bee Colony (IABC) for the upgradation of initial search results of ABC for Job Shop Scheduling Problem (JSSP). Eventually, their adopted strategy can be formulated by mutation, which was used to widen the search space along with local optima avoidance.…”

Section: Application Of Abc In Combinatorial Optimizationmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Application Of Abc In Combinatorial Optimizationmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As an NP-hard problem of resource allocation, metaheuristics and artificial intelligence methods have been developed over the years to produce optimal solutions. Some of these include taboo search (Watson et al, 2003), simulated annealing (Ponnambalam et al, 1999), genetic algorithm (Dellacroce et al, 1995;Wang and Tang, 2011), particle swarm optimization (Niu et al, 2008), ant colony optimization (Puris et al, 2007), bee colony optimization (Wong et al, 2010), and artificial bee colony (Yao et al, 2010). These methods are well studied and are successfully applied to generate solution in different domains.…”

Section: Introductionmentioning

confidence: 99%