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.