Fuzzified Particle Swarm Optimization Algorithm for Multi-area Security Constrained Economic Dispatch

Somasundaram, P.; Swaroopan, N. M. Jothi

doi:10.1080/15325008.2011.552094

Cited by 20 publications

(10 citation statements)

References 15 publications

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“…Ajusting value can keep the best relationship between the global search and local search, so as to improve the performance of the proposed algorithm. If value is larger, the exploration ability of particle is enhanced [12][13][14]. This is conducive to the global search, and can avoid the local extremum, but not easy to get the accurate solution.…”

Section: Chaos Initializationmentioning

confidence: 99%

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

Jia¹,

Cai²,

Tian³

et al. 2014

TOCSJ

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Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, improve the probability of obtain optimal solution. Then, CM-PSO algorithm introduces nonlinear decreasing strategy on the inertia weight to avoid local optimal solution. In the experimental stage, four different functions are used to validate the performance of the algorithm. The experimental results show that, compared with the standard particle swarm algorithm, CM-PSO algorithm has strong global searching ability, can effectively avoid the premature convergence problem, and enhance the ability of the algorithm to escape from local optima. Although the algorithm consumes time is slightly increased, it is worth for getting the global optimal solution with such cost.

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Section: Chaos Initializationmentioning

confidence: 99%

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

Jia¹,

Cai²,

Tian³

et al. 2014

TOCSJ

View full text Add to dashboard Cite

show abstract

“…The aim of MAED is to minimize the total production cost while satisfying various physical and operational constraints [10]. In this section, we employ a two-area economic dispatch formulation to illustrate the MAED problem.…”

Section: The Traditional Maed Formulationmentioning

confidence: 99%

The Auxiliary Problem Principle with Self-Adaptive Penalty Parameter for Multi-Area Economic Dispatch Problem

Ren¹,

Fei²

2015

Algorithms

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Abstract:The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method.

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“…However, DCIPM requires that each area must use interior point method, this is difficult to achieve in practice. Particle swarm optimization (PSO) is introduced to eliminate the MAED problem [3]. The positive aspect of PSO is that it only depends on the objective function information, i.e.…”

Section: Introductionmentioning

confidence: 99%

Prediction-Correction Alternating Direction Method for Power Systems Economic Dispatch

Ren¹,

Fei²,

Wei³

2015

IJCEE

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Abstract:The alternating direction method is a powerful tool for solving multi-area economic dispatch problem. However, plenty of applications show that the choice of step size and penalty parameter has an important influence on algorithm's convergence rate. In this paper, we proposed a novel prediction-correction algorithm. To be more exact, the prediction is generated by original alternating direction method, while the correction is implemented via adjusting step size and penalty parameter. Numerical results illustrate the proposed method is robust.

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Fuzzified Particle Swarm Optimization Algorithm for Multi-area Security Constrained Economic Dispatch

Cited by 20 publications

References 15 publications

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

The Auxiliary Problem Principle with Self-Adaptive Penalty Parameter for Multi-Area Economic Dispatch Problem

Prediction-Correction Alternating Direction Method for Power Systems Economic Dispatch

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