Guided Moth–Flame optimiser for multi-objective optimization problems

Djaafar Zouache (), Fouad Ben Abdelaziz (), Mira Lefkir () and Nour El-Houda Chalabi ()
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Djaafar Zouache: University of Mohamed El Bachir Elibrahimi
Fouad Ben Abdelaziz: NEOMA Business School, Rouen Campus
Mira Lefkir: University of Mohamed El Bachir Elibrahimi
Nour El-Houda Chalabi: University of Mohamed El Bachir Elibrahimi

Annals of Operations Research, 2021, vol. 296, issue 1, No 34, 877-899

Abstract: Abstract This paper proposes a novel version of Moth–Flame optimiser for solving multi-objective problems (MOMFO). The main idea of this algorithm is that the Moth’s swarm explores the search space around the Flames set (leader solutions). To implement our approach, we integrate the unlimited external archive to guide the Moth’s swarm during the exploration search to find the Pareto solutions set. To ensure a good compromise between convergence and diversity when exploring the search space, we use the epsilon dominance principle to update the external archive. In addition, we use the non-dominated sort and crowding distance in updating the Flame solutions to ensure rapid convergence towards the Pareto solutions set. We validate the proposed algorithm on twelve test functions and compare our results with two well-known meta-heuristics. The results of the proposed algorithm show better convergence behavior with a better diversity of solutions.

Keywords: Moth–Flame optimiser; Multi-objective optimization; Pareto solutions set; Swarm intelligence; Epsilon dominance relation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10479-019-03407-8

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