A genetic algorithm – differential evolution based hybrid framework: Case study on unit commitment scheduling problem

Abstract

This research article proposes a hybrid evolutionary framework based on hybridization of genetic algorithm (GA) and differential evolution (DE) for solving a nonlinear, high-dimensional, highly constrained, mixed-integer optimization problem called the unit commitment (UC) problem. Although GA is more capable of efficiently handling binary variables, the performance of DE is better in real parameter optimization. Thus, in the proposed hybrid framework, termed hGADE, the binary variables are evolved using GA while the continuous variables are evolved using DE. To test the efficiency of the presented framework, GA is hybridized with 4 classical and 2 state-of-the-art self-adaptive DE variants. We also incorporate a heuristic initial population generation method and a replacement scheme based on preserving infeasible solutions in the population to enhance the performance of the hGADE variants. A systematic classification of the proposed hybrid optimizer is presented in accordance with a recently proposed taxonomy in the literature. Extensive case studies are presented on different test systems and the effectiveness of the heuristic initialization, the replacement scheme, and the hybrid strategy is verified through stringent simulated results. We perform exhaustive benchmarking against some of the best algorithms proposed in the literature for UC problem to demonstrate the efficiency of the hGADE variants. Furthermore, the proposed hGADE variants are statistically compared among themselves to determine the best hGADE variants. Additionally, GA and DE are hybridized within multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework and the effectiveness of hybridization is demonstrated on multi-objective UC problem as well. The proposed hybrid framework is generic and other discrete and/or real parameter operators can be easily incorporated within the framework for solving different mixed-integer optimization problems.