Cuckoo search algorithm for non‐convex economic dispatch

Vo, Dieu Ngoc; Schegner, Peter; Ongsakul, Weerakorn

doi:10.1049/iet-gtd.2012.0142

Cited by 166 publications

(95 citation statements)

References 36 publications

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“…Compared to popular methods such as PSO, GA, TSA (Tabu search algorithm), and FA, the proposed method is better in terms of lower cost and lower N FES . On the contrary, the proposed method is less effective than remaining methods in [13,15,16,19]. As we set N FES to higher values such as 3000, 7000, and 10,000, the results obtained are also worse than these methods.…”

Section: Comparisons Of Test Casementioning

confidence: 89%

“…For some optimization algorithms possessing two new solution generations such as CSA [15] and ICSA [16], ω is 2 while for others possessing one new solution generation like PSO [11] and DE [11], ω is only 1. For the proposed IFA, there is only one new solution generation in each iteration; thus, ω is also equal to 1.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…As a result, the load demand-supply balance constraint can be dealt successfully by using the dependent variable P N,i obtained by the below model [15].…”

Section: The Second Proposed Improvementmentioning

confidence: 99%

“…The comparisons of search speed have not been carried out. The performance of cuckoo search algorithm (CSA) [14,15] and improved cuckoo search algorithm (ICSA) [16] have been shown clearly since different types of constraints were considered and wide comparisons were implemented. However, the speed comparisons have been made only via execution time, leading to not good comparison criteria for conclusion.…”

Section: Introductionmentioning

confidence: 99%

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Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

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Section: Comparisons Of Test Casementioning

confidence: 89%

Section: Comparison and Discussionmentioning

confidence: 99%

“…As a result, the load demand-supply balance constraint can be dealt successfully by using the dependent variable P N,i obtained by the below model [15].…”

Section: The Second Proposed Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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“…So far, a large number of methods have been successfully applied for dealing with the five cases of the problem in which methods applied to the case of multi-fuel options are conventional Hopfield neural network (HNN) [3], hierarchical approach (HA) [4], adaptive Hopfield neural network (AHNN) [5], improved Lagrangian neural network (ILNN) [6], hybrid real-coded genetic algorithm (HRCGA) [7], differential evolution (DE) [8], modified evolutionary programming (MEP) [9], artificial immune system algorithm (AIS) [10] and hybrid differential evolution and dynamic programming (HDEDP) [11], and cuckoo search algorithm (CSA) [12]. Among these methods, ones based on neural network and numerical method have the same disadvantages, such as the hard task of tuning control parameters and stopping application for systems with non-differentiable functions.…”

Section: Introductionmentioning

confidence: 99%

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Nguyen

Quynh

et al. 2018

Energies

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Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

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