A Cooperative Co-evolutionary LSHADE Algorithm for Large-Scale Global Optimization

Abstract

In this paper, we propose the application of a Cooperative Co-evolutionary LSHADE (CCLSHADE) algorithm for Large-Scale Global Optimization (LSGO). We illustrate that by tuning two simple parameters of the CC framework, one can obtain very competitive results. The results are achieved without the need of incorporating local search modules, a re-initialization step, or adaptively configuring the CC framework budget allocation. The two parameters studied in this work are the number of iterations for which to run each sub-optimizer in a single cycle and the maximum size of the component containing the separable problem variables. The performance of CCLSHADE is compared against six state-of-the-art algorithms developed for LSGO using the CEC10 benchmarks. Experimental results and statistical tests confirm the competitiveness of the proposed algorithm.