Abstract
As known, most of the combinatorial optimization problems are NP-hard in terms of complexity, and they are solved as part of one of the three predefined classifications: solution construction, solution improvement (or trajectory algorithms), and population-based metaheuristics. It is also known that it is practically very difficult to have both an optimal solution quality and a reduced computation time. Indeed, most conventional algorithms make the choice between a high quality of the solution and an exponential computation time, or a solution of modest quality and a polynomial time. The third choice offers a good (not necessarily optimal) solution in a reasonable computation time.
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Ouaarab, A. (2020). Solving Combinatorial Optimization Problems. In: Discrete Cuckoo Search for Combinatorial Optimization. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-3836-0_3
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DOI: https://doi.org/10.1007/978-981-15-3836-0_3
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