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

Abedinia, Oveis; Amjady, Nima; Ghasemi, Ali; hejrati, Zakariya

doi:10.1002/etep.1674

Cited by 36 publications

(26 citation statements)

References 26 publications

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“…The best result archived by the MPSO-TVAC for 6-generators system is compared with the previous publiccations of GA [14], PSO [14], PSO_LRS [16], NPSO [16], NPSO_LRS [16] and PSO-TVAC [36] in Table 8. The results show that the MPSO-TVAC provides the minimum cost with less computational time compared to other methods .…”

Section: Comparison Of Best Solutionmentioning

confidence: 99%

“…The results show that the MPSO-TVAC provides the minimum cost with less computational time compared to other methods . For 15-generators system, the results obtained by the MPSO-TVAC are compared with GA [14], PSO [14], BF [37], SOH_PSO [17],GA-API [6], PSO-MSAF [38], PSO-TVAC [36] and FA [34] in Table 9. It shows that MPSO-TVAC can produce a better cost and less computational time compared to other methods.…”

Section: Comparison Of Best Solutionmentioning

confidence: 99%

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Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

Abdullah

Bakar

Rahim

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper proposes a Modified Particle Swarm Optimization with Time VaryingAcceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called 'rbest' is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.

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Section: Comparison Of Best Solutionmentioning

confidence: 99%

Section: Comparison Of Best Solutionmentioning

confidence: 99%

Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

Abdullah

Bakar

Rahim

et al. 2014

Journal of Electrical Engineering and Technology

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show abstract

“…Many of real-world optimization problems involve with complexities such as nonlinearity, nonconvexity, nonsmoothness, nondifferentiability, mixed integer nature, and discontinuous domain, which challenge the numerical optimization methods [3]. Accordingly, to tackle the mentioned complexities, several metaheuristic optimization techniques have been proposed in the literature in the recent decades such as genetic algorithm (GA) [4], particle swarm optimization (PSO) [5,6], ant colony optimization (ACO) [7,8], honey bee mating optimization (HBMO) [9,10], artificial bee colony (ABC) [11,12], bacterial foraging (BF) [13], clonal selection algorithm (CSA) [14], invasive weed optimization (IWO) [15], shuffled frog leaping (SFL) [16], evolutionary algorithm (EA) [17], differential evolution (DE) [18], Correspondence to: O. Abedinia, E-mail: abediniaoveis@ gmail.com simulated annealing (SA) [19], and gravitational search algorithm (GSA) [20,21]. Due to their high flexibility, simplicity and modeling efficiency, these optimization methods have been widely used in many scientific and engineering areas.…”

Section: Introductionmentioning

confidence: 99%

A new metaheuristic algorithm based on shark smell optimization

2014

Self Cite

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In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real-world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm.

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“…However, the classical PSO algorithm may converge at local minima, especially for complex problems with multiple local minima. Many PSO variants were proposed in [21][22][23][24][25] in order to enhance the searching capability of classical PSO.…”

Section: Introductionmentioning

confidence: 99%

Modified particle swarm optimization for economic-emission load dispatch of power system operation

Abdullah

Bakar²,

Rahim³

et al. 2015

Turk J Elec Eng & Comp Sci

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This paper proposes a modified particle swarm optimization considering time-varying acceleration coefficients for the economic-emission load dispatch (EELD) problem. The new adaptive parameter is introduced to update the particle movements through the modification of the velocity equation of the classical particle swarm optimization (PSO) algorithm. The idea is to enhance the performance and robustness of classical PSO. The price penalty factor method is used to transform the multiobjective EELD problem into a single-objective problem. Then the weighted sum method is applied for finding the Pareto front solution. The best compromise solution for this problem is determined based on the fuzzy ranking approach. The IEEE 30-bus system has been used to validate the effectiveness of the proposed algorithm.It was found that the proposed algorithm can provide better results in terms of best fuel cost, best emissions, convergence characteristics, and robustness compared to the reported results using other optimization algorithms.

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

Cited by 36 publications

References 26 publications

Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

A new metaheuristic algorithm based on shark smell optimization

Modified particle swarm optimization for economic-emission load dispatch of power system operation

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