Nondominated sorting-based disruption in oppositional gravitational search algorithm for stochastic multiobjective short-term hydrothermal scheduling

• Published in: Soft computing (Berlin, Germany) Vol. 23; no. 16; pp. 7229 - 7248
• Main Authors: Nadakuditi, Gouthamkumar, Balusu, Srinivasarao, Bathina, Venkateswararao, Narasimham, P. V. R. L.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Decision making; Disruption; Electric power; Engineering; Expected values; Genetic algorithms; Hydraulics; Mathematical analysis; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Multiple objective analysis; Operators (mathematics); Optimization; Random variables; Robotics; Scheduling; Search algorithms; Search process; Sorting algorithms; Stochastic models; Water transportation; Spread indicator; Gravitational search algorithm; Stochastic multiobjective optimization; Stochastic hydrothermal scheduling; Disruption operator; Fuzzy decision making; Multi-cascaded reservoir; Opposition-based learning
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-018-3368-6

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.