GA and PSO culled hybrid technique for economic dispatch problem with prohibited operating zones

Sudhakaran, M.; Ajay-D-Vimalraj, P.; Palanivelu, T.G.

doi:10.1631/jzus.2007.a0896

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…The results show that the proposed hybrid PSOTVAC/BFA obtains the minimum cost of $15 441.0556/h as compared with the other methods. Similarly, the result of the proposed method demonstrates that for the 15-unit system, $32 604.3714/h is the minimum cost, which is the lowest cost in comparison with PSO [18], hybrid GAPSO [19], SOH_PSO [20], CPSO1 and CPSO2 [18], and IPSO [18]. For the 40-unit system, $121 070.643/h is the minimum achieved cost that is compared with SPSO [20], PSO_LRS [28], PC_PSO [20], NPSO [20], NPSO_LRS [28], SOH_PSO [20], and BBO [17] mentioned in Table III.…”

Section: Numerical Resultsmentioning

confidence: 77%

Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques

Abedinia

Amjady

Ghasemi

et al. 2012

Int. Trans. Electr. Energ. Syst.

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SUMMARY In this research, a hybrid particle swarm optimization with time‐varying acceleration coefficients (HPSOTVAC) and bacteria foraging algorithm (BFA) are presented for solving a complex economic load dispatch problem. Basically, there are many realistic constraints that affect feasible operation such as generation limitation, ramp rate limits, prohibited operating zone, nonlinear cost functions, and transmission loss that are considered in this research. The effectiveness of the proposed HPSOTVAC/BFA is tested in 6‐, 15‐, and 40‐unit generating systems. Also, for 6‐unit case study, the valve‐point effect is considered too. The numerical results demonstrate that the proposed hybrid technique gives less total generation costs than genetic algorithm (GA), particle swarm optimization (PSO), hybrid GA/PSO, active power optimization, iteration PSO, and self‐organizing hierarchical PSO methods. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Numerical Resultsmentioning

confidence: 77%

Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques

Abedinia

Amjady

Ghasemi

et al. 2012

Int. Trans. Electr. Energ. Syst.

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“…This inertia weight could also linearly decrease with respect to time [23,31,32]. Generally for initial stages of the search process, large inertia weight to enhance the global exploration is recommended while, for last stages, the inertia weight is reduced for local exploration [25,28,33]. The mathematical expression could be as follows:…”

Section: Particlesmentioning

confidence: 99%

A New Hybrid Algorithm to Solve Winner Determination Problem in Multiunit Double Internet Auction

Ykhlef

Alqifari²

2015

Mathematical Problems in Engineering

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Solving winner determination problem in multiunit double auction has become an important E-business task. The main issue in double auction is to improve the reward in order to match the ideal prices and quantity and make the best profit for sellers and buyers according to their bids and predefined quantities. There are many algorithms introduced for solving winner in multiunit double auction. Conventional algorithms can find the optimal solution but they take a long time, particularly when they are applied to large dataset. Nowadays, some evolutionary algorithms, such as particle swarm optimization and genetic algorithm, were proposed and have been applied. In order to improve the speed of evolutionary algorithms convergence, we will propose a new kind of hybrid evolutionary algorithm that combines genetic algorithm (GA) with particle swarm optimization (PSO) to solve winner determination problem in multiunit double auction; we will refer to this algorithm as AUC-GAPSO.

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“…N PZi is the prohibited zones number of i'th generator; N GPz is the sum of generators with prohibited operating zones [16,21].…”

Section: Economic Load Dispatch Problem Formulationmentioning

confidence: 99%

“…Among these intelligent methods, particle swarm optimization (PSO) is simple and appealing which has some advantages such as few parameters to adjust, high accuracy, less computational time and has gained popularity as the best suitable solution. However, classical PSO still has some problems such as premature convergence, dependence to it's parameters and trapping in local optima [16][17][18]. Many researchers have been proposed different variations to improve the quality search of PSO.…”

Section: Introductionmentioning

confidence: 99%

Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

Zare¹,

Bolandi²

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

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This paper proposes a novel technique named modified iteration particle swarm optimization for finding possible optimal solution in the economic load dispatch problem considering non-convex and non-smooth fuel cost function. Many constrains such as ramp rate limits, transmission losses and prohibited operating zones are considered. The valve point effects with nonlinear cost function in non-convex economic dispatch are also considered. Classical particle swarm optimization (CPSO) highly depends on its parameters and often suffers from being involved in local optima. The proposed approach incorporates two new index named "first iteration best" and "second iteration best" in CPSO. It results that searching behavior is enhanced and solution quality is promoted. To ensure the efficiency of the proposed approach, it is applied for four test system including 3, 6, 13 and 15 units. From the simulation results, the capabilities and effectiveness of the proposed approach compared with existing methods is confirmed.

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GA and PSO culled hybrid technique for economic dispatch problem with prohibited operating zones

Cited by 17 publications

References 17 publications

Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques

Solution of economic load dispatch problem via hybrid particle swarm optimization with time-varying acceleration coefficients and bacteria foraging algorithm techniques

A New Hybrid Algorithm to Solve Winner Determination Problem in Multiunit Double Internet Auction

Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

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