Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II
By Malabika Basu. 2008
427 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/2385aafa47112dfc715475364657658b9c6fcb7c
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In Artificial Intelligence
In Computational Theory and Mathematics
In Electrical and Electronic Engineering
Citations in the corpus
- A multi-objective chaotic ant swarm optimization for environmental/economic dispatch (2010, methodology): “Different techniques have been reported in the literature pertaining to EED problem, including conventional approaches such as weightedminimaxmethod [6], direct analytical solutionmethod [7], linear programming [7–9] and 1-constraint method [10], and artificial intelligence technology such as geneti”
- Policy invariance under reward transformations for multi-objective reinforcement learning (2017, methodology): “The version of the problem which we analyse here was originally proposed by Basu [31].”
- A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch (2012, methodology): “Several multi-objective evolutionary algorithms such as the niched Pareto genetic algorithm (NPGA) [2], the nondominated sorting genetic algorithm (NSGA) [3], the strength Pareto evolutionary algorithm (SPEA) [4] and the NSGA-II [7,29] have been introduced to solve the EED problem.”