Cooperative Co-evolution for large scale optimization through more frequent random grouping

Abstract

In this paper we propose three techniques to improve the performance of one of the major algorithms for large scale continuous global function optimization. Multilevel Cooperative Co-evolution (MLCC) is based on a Cooperative Co-evolutionary framework and employs a technique called random grouping in order to group interacting variables in one subcomponent. It also uses another technique called adaptive weighting for co-adaptation of subcomponents. We prove that the probability of grouping interacting variables in one subcomponent using random grouping drops significantly as the number of interacting variables increases. This calls for more frequent random grouping of variables. We show how to increase the frequency of random grouping without increasing the number of fitness evaluations. We also show that adaptive weighting is ineffective and in most cases fails to improve the quality of found solution, and hence wastes considerable amount of CPU time by extra evaluations of objective function. Finally we propose a new technique for self-adaptation of the subcomponent sizes in CC. We demonstrate how a substantial improvement can be gained by applying these three techniques.

Initialization methods for large scale global optimization

“…As a fact, all participants of previous competitions on LSGO held at the IEEE Congress on Evolutionary Computation (CEC-2008, CEC-2010 and CEC-2012), except [15], simply used RNGs as initialization methods. Considering the growing demands on solving LSGO problems [16], [17], it is important to investigate whether advanced initialization methods are able to improve the performance of state-of-theart EAs in comparison to basic RNGs.…”

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

“…MLCC [32] is further improved by Yang et al [14]. They introduced 3 techniques to improve its performance.…”

Constructive cooperative coevolution for large-scale global optimisation

“…Random grouping has been proposed to increase the performance on non-separable high dimensional problems (Omidvar et al 2010;Yang et al 2008). With random grouping, the parameters are frequently regrouped to increase the chance of having interacting parameters in the same subproblem.…”

“…CC has a good performance especially on large-scale optimisation problems. Though, other works (Li and Yao 2009;Omidvar et al 2014Omidvar et al , 2010Potter and De Jong 1994;Ray and Yao 2009;Yang et al 2008) found that the CC struggles to solve non-separable problems (i.e. interactions/interdependencies between parameters).…”