Partial decomposition and parallel GA (PD-PGA) for constrained optimization

Large scale constrained optimization problem solving is a challenging research topic in the optimization and computational intelligence domain. This paper examines the possible division of computational tasks, into smaller interacting components, in order to effectively solve constrained optimization problems in the continuous domain. In dividing the tasks, we propose problem decomposition, and the use of GAs as the solution approach. In this paper, we consider problems with block angular structure with or without overlapping variables. We decompose not only the problem but also the chromosome as suitable for different components of the problem. We also design a communication process for exchanging information between the components. The research shows an approach of dividing computation tasks, required in solving large scale optimization problems, which can be processed in parallel machines. A number of test problems have been solved to demonstrate the use of the proposed approach. The results are very encouraging.