Evolutionary programming techniques for economic load dispatch

By N. Sinha, R. Chakrabarti, Pranab Kumar Chattopadhyay. IEEE Transactions on Evolutionary Computation, 2003

1,354 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2e4f9d0d7e412f853a97dbdefd9f700b85d19c0a

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Citations in the corpus

A particle swarm optimization for economic dispatch with nonsmooth cost functions (2005, methodology): “To solve this problem, many salient methods have been proposed such as a mathematical approach [7], dynamic programming [8], evolutionary programming [9], [15], [16], Tabu search [14], neural network approaches [10], [11], and genetic algorithm [12].”
Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems (2012, neutral): “This test case consists of 40 generating units with valve-point effects [42].”
Economic dispatch using particle swarm optimization: A review (2009, background): “Modern heuristic optimization techniques proposed by researchers based on operational research and artificial intelligence concepts, such as evolutionary programming [13–16], genetic algorithm [17–21], simulated annealing [22–24], ant colony optimization [25,26], Tabu search [27,28], neural network ”
A GA-API Solution for the Economic Dispatch of Generation in Power System Operation (2012, methodology): “As far as DP is concerned, this method does not impose any restrictions on the structure of the optimization system but it suffers from the “curse of dimensionality” [8], especially when the number of system variables increases.”
Economic dispatch using chaotic bat algorithm (2016, background): “It is to be noted that the value of the ej coefficient in the cost equation of generator 3 is assumed to be 200 in this paper (as in [49]), but this value is assumed to be 150 in [43].”
Biogeography-Based Optimization for Different Economic Load Dispatch Problems (2010, methodology): “Their best solutions are shown in Table I. The convergence characteristic of the six-generator systems in case of BBO algorithm is shown in Fig. 3. 2) Test Case 2: A system with 40 generators with valve point loading is used here. The input data are given in [ 16 ].”
Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects (2008, methodology): “In this context, a 13-units test system [23] with incremental fuel cost function that takes into account the valve-point loading effects is used to illustrate the effectiveness of the proposed HQPSO method.”