Authors: Najlawi Bilel; Nejlaoui Mohamed
Addresses: Laboratory of Mechanical Engineering, National Engineering School of Monastir, University of Monastir, Avenue IBN Eljazzar, Monastir 5000, Tunisia ' Laboratory of Mechanical Engineering, National Engineering School of Monastir, University of Monastir, Avenue IBN Eljazzar, Monastir 5000, Tunisia
Abstract: Solving engineering design and resources optimisation via multi-objective evolutionary algorithms has attracted much attention in the last few years. In this study, an improved Self-Organising Migrating Algorithm (MOSOMA) is developed and investigated to solve multi-objective engineering design problems. The proposed MOSOMA algorithm uses a migration approach for the search of optima. In order to obtain a uniform distribution of Pareto optimal solutions, the crowding distance method is introduced. Pareto dominance is incorporated into the algorithm in order to allow this heuristic to handle problems with several objective functions. The performance of the MOSOMA algorithm is assessed by applying it to a set of multi-objective standard test functions and constrained engineering design problems. The results show that the proposed approach is competitive and effective compared to other algorithms contemplated in this work and it can also find the result with greater precision.
Keywords: multi-objective optimisation; self-organising migrating algorithm; test problem; Pareto front.
International Journal of Computer Applications in Technology, 2018 Vol.57 No.3, pp.219 - 227
Received: 07 Sep 2016
Accepted: 07 Mar 2017
Published online: 25 Jun 2018 *