Hybrid algorithm based on EP and LP for security constrained economic dispatch problem

Somasundaram, P.; Lakshmiramanan, R.; Kuppusamy, K.

doi:10.1016/j.epsr.2005.04.005

Cited by 15 publications

(11 citation statements)

References 16 publications

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“…Somasundaram et al [61] proposed a hybrid method for solving the security constrained economic dispatch problem [66]. A simple evolutionary programming is applied as a base level search, which can give a good direction to the optimal global region and a local search linear programming (LP) is used to fine tune to determine the optimal solution.…”

Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

“…A simple evolutionary programming is applied as a base level search, which can give a good direction to the optimal global region and a local search linear programming (LP) is used to fine tune to determine the optimal solution. For the problem considered, authors found [61] that initially, the rate of convergence in EP is very fast and subsequently the convergence is very slow. A direct LP approach will be effective only when the magnitudes of constraint violations are less and corrections in the control variables are small.…”

Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

“…A direct LP approach will be effective only when the magnitudes of constraint violations are less and corrections in the control variables are small. In order to overcome these difficulties, a two-phase hybrid method has been proposed by the authors [61].…”

Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

“…For several problems a simple Evolutionary algorithm might be good enough to find the desired solution. As reported in the literature, there are several types of problems where a direct evolutionary algorithm could fail to obtain a convenient (optimal) solution [37,40,61,65]. This clearly paves way to the need for hybridization of evolutionary algorithms with other optimization algorithms, machine learning techniques, heuristics etc.…”

Section: Introductionmentioning

confidence: 98%

See 3 more Smart Citations

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

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Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

Section: Hybrid Approaches Incorporating Local Search and Othersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

View full text Add to dashboard Cite

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“…Some of these methods use the genetic algorithm [1,2], the particle swarm optimization technique [3,4], the tabu search technique [5,6], the evolutionary, differential evolutionary and the modified differential evolutionary algorithms [7][8][9][10], the pseudo spot price algorithm [11], the interior point method [12], the quantum-inspired evolutionary algorithm [13], and finally the hybrid methods [14].…”

Section: Introductionmentioning

confidence: 99%

A solution to security constrained non-convex economic dispatch problem by modified subgradient algorithm based on feasible values

Fadıl¹,

Yazıcı²,

Urazel³

2012

International Journal of Electrical Power & Energy Systems

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Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

Rajagopalan

Kasinathan²,

Karthik

et al. 2019

Int Trans Electr Energ Syst

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Summary The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self‐adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11‐generating units (without security), IEEE‐30 bus system, and IEEE‐118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.

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Hybrid algorithm based on EP and LP for security constrained economic dispatch problem

Cited by 15 publications

References 16 publications

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

A solution to security constrained non-convex economic dispatch problem by modified subgradient algorithm based on feasible values

Chaotic self‐adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

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