With the ever increasing demand and stressed operating conditions, resource expansion is the only way to have sustainable electric grid. Transmission system expansion is one of the important aspects in this regard. In...
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With the ever increasing demand and stressed operating conditions, resource expansion is the only way to have sustainable electric grid. Transmission system expansion is one of the important aspects in this regard. In the recent years, expansion problem has been addressed by several researchers. Meta-heuristic techniques have been applied to solve expansion problems. In this paper, a new variant of teachinglearningbasedoptimization (TLBO) algorithm is proposed by adding a sine function based diversity in the teaching phase. The proposed variant is named as Composite TLBO (C-TLBO). The efficacy of the proposed variant has been evaluated on standard benchmark functions and then it is evaluated on two standard electrical networks with cases of inclusion of uncertainty and demand burst. The results obtained from optimization processes have been evaluated with the help of several analytical and statistical tests. Results affirm that the proposed modification enhances the performance of the algorithm in a substantial manner.
Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method o...
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Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution algorithm (EDA) with teaching and learning-basedoptimizationalgorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.
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