In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching solution neighbors to improve the quality of global solutions. This part of the algorithm is used to search sparsely populated areas for finding the dominant solutions. To improve the algorithm, for each firefly some changes have been made on the criteria of determining the global optimal solution and doing local optimal solution; this leads to more uniformity of the Pareto curve and error reduction, as the experimental results show. The proposed algorithm is an extension of a basic algorithm.
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Salimisartaghti, J., & Goli, . (2021). A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms. Soft Computing Journal, 8(1), 14-28. doi: 10.22052/8.1.14
MLA
Javad Salimisartaghti; گلی Goli. "A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms", Soft Computing Journal, 8, 1, 2021, 14-28. doi: 10.22052/8.1.14
HARVARD
Salimisartaghti, J., Goli, . (2021). 'A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms', Soft Computing Journal, 8(1), pp. 14-28. doi: 10.22052/8.1.14
VANCOUVER
Salimisartaghti, J., Goli, . A Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms. Soft Computing Journal, 2021; 8(1): 14-28. doi: 10.22052/8.1.14
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