PROJECT TITLE :

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

ABSTRACT:

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) downside as a multiobjective optimization downside (MOP) considering minimizing value and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated inside the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, whereas differential evolution (DE) evolves the continuous variables. Additional, a completely unique nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm primarily based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to boost the performance of the presented algorithm. Intensive case studies are presented on totally different check systems and therefore the effectiveness of the hybrid strategy, the NUWD strategy, and also the ensemble algorithm is verified through stringent simulated results. Additional, exhaustive benchmarking against the algorithm proposed within the literature is presented to demonstrate the superiority of the proposed algorithm.


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