Enhanced Multiobjective Evolutionary Algorithm Based on Decomposition for Solving the Unit Commitment Problem

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem as a multiobjective optimization problem (MOP) considering minimizing cost and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, while differential evolution (DE) evolves the continuous variables. Further, a novel nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to enhance the performance of the presented algorithm. Extensive case studies are presented on different test systems and the effectiveness of the hybrid strategy, the NUWD strategy, and the ensemble algorithm is verified through stringent simulated results. Further, exhaustive benchmarking against the algorithm proposed in the literature is presented to demonstrate the superiority of the proposed algorithm.