Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems. (August 2017)

• Record Type: Journal Article
• Authors: Savsani, Vimal; Tawhid, Mohamed A.
• Is Part Of: Engineering applications of artificial intelligence. Volume 63(2017:Mar.)
• Page Start: 20
• Page End: 32
• Publication Date: 2017-08
• Subjects: Multi-objective optimization -- Moth-flame optimization algorithm -- Multi-objective engineering design problems; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.04.018
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper proposes an effective non-dominated moth-flame optimization algorithm (NS-MFO) method for solving multi-objective problems. Most of the multi-objective optimization algorithms use different search techniques inspired by different optimization techniques such as genetic algorithms, differential evolutions, particle swarm optimization, cuckoo search etc., but search techniques of recently developed metaheuristics have hardly been investigated. Non-dominated moth-flame optimization algorithm (NS-MFO) is based on the search technique of moth-flame optimization algorithm (MFO) algorithm and utilizes the elitist non-dominated sorting and crowding distance approach for obtaining different non domination levels and to preserve the diversity among the optimal set of solutions respectively. The effectiveness of the method is measured by implementing it on multi-objective benchmark problems and multi-objective engineering design problems with distinctive features. It is shown in this paper that this method effectively generates the Pareto front and also, this method is easy to implement and algorithmically simple. Highlights: We develop a novel Non-dominated sorting moth flame optimization (NS-MFO). Efficiency of proposed algorithm tested on multi-objective engineering design problems. Test NS-MFO various characteristics of Pareto front as convex/non-convex/discrete. We show NS-MFO effectively generates the Pareto front and is easy to implement.