Enhanced Multiobjective Evolutionary Algorithm based on Decomposition for Solving the Unit Commitment Problem
作者： Anupam Trivedi , Kunal Pal , Dipti Srinivasan , Chiranjib Saha
DOI:
关键词: Mathematical optimization 、 Meta-optimization 、 Population-based incremental learning 、 Cultural algorithm 、 Computer science 、 Evolutionary algorithm 、 Differential evolution 、 Interactive evolutionary computation 、 Genetic algorithm 、 Optimization problem 、 Genetic representation
摘要: The unit commitment (UC) problem is a nonlinear, high-dimensional, highly constrained, mixed-integer power system optimization and generally solved in the literature considering minimizing operation cost as only objective. However, due to increasing environmental concerns, recent attention has shifted incorporating emission formulation. In this paper, multi-objective evolutionary algorithm based on decomposition (MOEA/D) proposed solve UC multiple objec- tives. Since, consisting of binary variables continuous dispatch variables, novel hybridization strategy within framework MOEA/D such that genetic (GA) evolves while differential evolution (DE) variables. Further, non-uniform weight vector distribution parallel island model combination with uniform implemented enhance performance presented algorithm. Extensive case studies are different test systems effectiveness strategy, verified through stringent simulated results. exhaustive benchmarking against algorithms demonstrate superiority obtaining significantly better converged uniformly distributed trade-off solutions.
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