Nondominated sorting-based disruption in oppositional gravitational search algorithm for stochastic multiobjective short-term hydrothermal scheduling
This article presents a novel framework of nondominated sorting-based disruption in oppositional gravitational search algorithm (NSDOGSA) for solving an inventive stochastic multiobjective short-term hydrothermal scheduling (SMSHTS) problems. An innovative SMSHTS mathematical formulation is derived in terms of stochastic objective functions subject to stochastic hydro- and thermal constraints. Also, a versatile constraint handling procedure is proposed to satisfy all the constraints. Besides, an opposition-based learning perception is assimilated in a gravitational search algorithm (GSA) to explore the excellence of the present population and disruption operator is integrated to hasten the convergence of solutions. Moreover, a nondominated sorting procedure is hybridized to attain a set of nondominated solutions for SMSHTS problems. In addition, an elite external archive is created to keep the nondominated solutions with the help of spread indicator and also to guide the search process toward global optima. Further, a fuzzy decision making is employed for selecting the best trade-off solution among the nondominated solution set. Finally, the proposed NSDOGSA approach is validated on four test systems that consist of two fixed-head and two variable-head stochastic multiobjective hydrothermal scheduling problems. Thus, the obtained simulation results are found to be better in terms of objective function values as well to satisfy constraints compared to other methods within a reasonable execution time.
KeywordsStochastic multiobjective optimization Multi-cascaded reservoir Stochastic hydrothermal scheduling Gravitational search algorithm Opposition-based learning Disruption operator Spread indicator Fuzzy decision making
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Conflict of interest
The authors declare that they have no conflict of interest.
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