Optimisation of multi-objective problems using an efficient Levy flight grasshopper algorithm

摘要

This paper is concerned in improving the performance of the nature-inspired multi-objective grasshopper optimisation algorithm (MOGOA). The Levy flight technique is used for the first time to boost MOGOA algorithm by providing better solution's diversity. The proposed Levy flight trajectory-based multi-objective grasshopper optimisation algorithm (LMOGOA) benefits from fast convergence speed to the true Pareto-optimal front and thus it overcomes MOGOA drawbacks of premature convergence and local optima stagnation. Then, LMOGOA algorithm is tested on a set of standard multi-objective problems (MOPs) and results proved significantly its out performance compared to MOGOA algorithm.