2021
DOI: 10.1007/978-3-030-73603-3_7
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A Data-Driven Dynamic Discretization Framework to Solve Combinatorial Problems Using Continuous Metaheuristics

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Cited by 11 publications
(10 citation statements)
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“…While some MHs operate on binary domains without a binary scheme, studies have demonstrated that continuous MHs supported by a binary scheme perform exceptionally well on multiple NP-hard combinatorial problems [ 1 ]. Examples of such MHs include the binary bat algorithm [ 28 , 29 ], particle swarm optimization [ 30 ], binary sine cosine algorithm [ 2 , 31 , 32 , 33 ], binary salp swarm algorithm [ 34 , 35 ], binary grey wolf optimizer [ 32 , 36 , 37 ], binary dragonfly algorithm [ 38 , 39 ], the binary whale optimization algorithm [ 2 , 32 , 40 ], and the binary magnetic optimization algorithm [ 41 ].…”
Section: Related Workmentioning
confidence: 99%
See 3 more Smart Citations
“…While some MHs operate on binary domains without a binary scheme, studies have demonstrated that continuous MHs supported by a binary scheme perform exceptionally well on multiple NP-hard combinatorial problems [ 1 ]. Examples of such MHs include the binary bat algorithm [ 28 , 29 ], particle swarm optimization [ 30 ], binary sine cosine algorithm [ 2 , 31 , 32 , 33 ], binary salp swarm algorithm [ 34 , 35 ], binary grey wolf optimizer [ 32 , 36 , 37 ], binary dragonfly algorithm [ 38 , 39 ], the binary whale optimization algorithm [ 2 , 32 , 40 ], and the binary magnetic optimization algorithm [ 41 ].…”
Section: Related Workmentioning
confidence: 99%
“…In the literature, various related works have proposed the hybridization of the sine cosine algorithm, grey wolf optimizer, whale optimization algorithm, and Q-learning [ 2 , 31 , 36 , 40 ]. Q-learning was used as a dynamic binarization scheme selector in each of the metaheuristics, allowing them to solve binary combinatorial problems.…”
Section: Related Workmentioning
confidence: 99%
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“…The study of metaheuristics has grown in recent years, with hybridizations emerging as the current trend. There exist hybridizations between metaheuristics such as those proposed in [2][3][4][5], hyperheuristic approaches where a high-level metaheuristic guides another low-level one [6][7][8], approaches where machine learning techniques enhance metaheuristics [9][10][11], and other approaches in which chaos theory is utilized to modify the stochastic behavior of metaheuristics [12][13][14][15].…”
mentioning
confidence: 99%