On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

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Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways. Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000 variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases.

论文关键词:Migrating birds optimization,Differential evolution,Large scale continuous problem,Global optimization,Leader replacement strategy,Continuous neighborhood search

论文评审过程:Received 9 November 2017, Revised 26 January 2018, Accepted 15 February 2018, Available online 21 February 2018, Version of Record 19 March 2018.

论文官网地址:https://doi.org/10.1016/j.eswa.2018.02.024