Large Scale Global Optimization: Experimental Results with MOS-based Hybrid Algorithms

Abstract—Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Through CEC 2005 to CEC 2013 competitions, many different algorithms have been proposed to solve continuous problems. The advances on this type of problems are of capital importance as many real-world problems from very different domains (bi-ology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. In this paper we describe the whole process of creating a competitive hybrid algorithm, from the experimental design to the final statistical validation of the results. We prove that a good experimental design is able to find a combination of algorithms that outperforms any of its composing algorithms by automatically selecting the most appropriate heuristic for each function and search phase. We also show that the proposed algorithm obtains statistically better results than the reference algorithm DECC-G