E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||5|
|Published online||24 May 2022|
An improved approach to resolve a combinatorial optimization problem based CoronaVirus Optimization Algorithm
1 Computer Sciences Department, FPL, Abdelmalek Essaâdi University, Larache, Morocco
2 Research Center STIS, M2CS, ENSAM, Mohammed V University, Rabat, Morocco
* Corresponding author: firstname.lastname@example.org
Combinatorial optimization problems refer to intractable problems that can’t be performed using exact methods. The resolution of combinatorial problems geared towards the application of heuristics, metaheuristics also matheuristics, in order to provide good enough approximations. As exact methods provide resolution corresponding to small problem scale, the approximation methods target large scale of complex problems. Metaheuristics are used to deploy intelligent methods to solve complex problems in a reasonable amount of time. The performance of a metaheuristic is improved by means of parameters adjustment as well as, hybridization within heuristics, iterative improvement methods or various metaheuristics. The cooperation of several optimization algorithms leads to improve resolution, also to overcome the limitations reported in resolving NP-hard problems. The resolution of complex problems, is thus constrained by stagnation on local optimums, as the optimization process is possibly stagnant on a specific search space region. In fact, traveling salesman problem is a combinatorial problem, that arises problematics related to the efficiency of its resolution methods. The aim of this work is to investigate on the improvement of a new bio-inspired method so-called coronavirus optimization algorithm in order to provide improved resolutions to traveling salesman problem. Various intelligent approaches are investigated and hybridized within coronavirus optimization algorithm, namely random replicate operator, elitist selective replicate operator, iterated local search, stochastic hill-climbing also improved self-organizing map. The numerical results are obtained using symmetric TSPLIB benchmarks.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.