Improving firefly-based multi-objective optimization based on attraction law and crowding distance

  • Farid shayesteh Department of Electrical Engineering, Islamic Azad University, Mashhad, Iran
  • Reihaneh Kardehi Moghaddam Department of Electrical Engineering, Islamic Azad University, Mashhad, Iran
DOI: https://doi.org/10.19139/soic-2310-5070-382
Keywords: Multi-objective Optimization, Crowding Distance, Firefly Algorithm, Law of Attraction, Pareto Front

Abstract

Multi-objective optimization problems are so designed that they simultaneously minimize several objectives functions (which are sometimes contradictory). In most cases, the objectives are in conflict with each other such that optimization of one objective does not lead to the optimization of another ones. Therefore, we should achieve a certain balance of goals to solve these problems, which usually requires the application of an intelligent method. In this regard, use of meta-heuristic algorithms will be associated with resolved problems. In this paper, we propose a new multi-objective firefly optimization method which is designed based on the law of attraction and crowding distance. The proposed methods efficiency has been evaluated by three valid test functions containing convex, nonconvex and multi discontinuous convex Pareto fronts. Simulation results confirm the significant accuracy of proposed method in defining the Pareto front for all three test functions. In addition, the simulation results indicates that proposed algorithm has higher accuracy and greater convergence speed, compared to other well known multi-objective algorithms such as non-dominated sorting genetic algorithm, Bees algorithm, Differential Evolution algorithm and Strong Pareto Evolutionary Algorithm.

Author Biographies

Farid shayesteh, Department of Electrical Engineering, Islamic Azad University, Mashhad, Iran
Department of Electrical Engineering, Islamic Azad University, Mashhad, Iran
Reihaneh Kardehi Moghaddam, Department of Electrical Engineering, Islamic Azad University, Mashhad, Iran
Department of Electrical Engineering, Islamic Azad University, Mashhad, Iran

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Published
2020-02-18
How to Cite
shayesteh, F., & Kardehi Moghaddam, R. (2020). Improving firefly-based multi-objective optimization based on attraction law and crowding distance. Statistics, Optimization & Information Computing, 8(1), 229-241. https://doi.org/10.19139/soic-2310-5070-382
Issue
Vol 8 No 1 (2020)
Section
Research Articles
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