Optimum cost of generation for maximum loadability limit of power system using hybrid particle swarm optimization

Shunmugalatha, A.; Slochanal, S. Mary Raja

doi:10.1016/j.ijepes.2008.04.001

Cited by 23 publications

(8 citation statements)

References 7 publications

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“…Among these works, studies on hybrid systems that combine skilled metaheuristic optimization algorithms to obtain good compromise between exploration and exploitation have gained extensive popularity. e most classical PSO variants have been reported in [14,15,23,[28][29][30][31][32][33][34][35][36]. Kao and Zahara [28] proposed the hybridization strategy of PSO and GA (GAPSO) for solving multimodal test functions.…”

Section: State-of-the-art Pso and Gsa Hybrid Variantsmentioning

confidence: 99%

“…Esmin et al [29] introduced a PSO algorithm coupled with GA mutation operator, namely, HPSOM, for solving unconstrained global optimization problems. Shunmugalatha and Slochanal [30] proposed a hybrid particle swarm optimization (HPSO), which incorporated the crossover, mutation operators, and subpopulation process in the genetic algorithm into particle swarm optimization. e implementation of HPSO on test functions shows that it converges to better solution much faster.…”

Section: State-of-the-art Pso and Gsa Hybrid Variantsmentioning

confidence: 99%

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Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

Jiang

Zhang

Chen

2020

Mathematical Problems in Engineering

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Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

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Section: State-of-the-art Pso and Gsa Hybrid Variantsmentioning

confidence: 99%

Section: State-of-the-art Pso and Gsa Hybrid Variantsmentioning

confidence: 99%

Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

Jiang

Zhang

Chen

2020

Mathematical Problems in Engineering

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“…Another is the global extreme value G=(g 1 , g 2 , …, g d ), the current optimal solution of particle group, and particles update their speeds and locations according to Eqns. (1) and (2) [9] :…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Application of improved PSO to power transmission congestion management optimization model

Liu

Yang

2008

J. Cent. South Univ. Technol.

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The parameters of particles were encoded firstly, then the constraint conditions and fitness degree were processed, and the calculation steps of the improved PSO algorithm were presented. Finally, the issues with the adoption of the improved PSO algorithm were solved and the results were analyzed. The results show that it is beneficial to obtaining the optimal solution by increasing the number of particles but that will also increase the operation time. On the aspects of solving continuous differentiable non-linear optimization model with equality and inequality constraints, the optimization result of PSO algorithm is the same as that of the interior point method. Compared with genetic algorithms (GA), PSO algorithm is more effective in the local optimization, and unlike GA, it will not be early maturity. Meanwhile, PSO algorithm is also more effective in the boundary optimization than genetic algorithm.

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“…This MAHPSO has the advantages of both HPSO and MAPSO. The optimal allocation of generators at this maximum loading point is determined in [10]. In this algorithm, the load is uniformly increased in all the load buses until the voltage limits are violated.…”

Section: Introductionmentioning

confidence: 99%

“…The computational algorithm of DE is simple to understand and implement. Only a few parameters are required to be set by the users [10][11][12]. Even though DE is shown to be precise, fast as well as robust, the faster convergence yields in a higher probability searching towards a local optimum or getting premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization

Gnanambal¹,

Babulal

2012

International Journal of Electrical Power & Energy Systems

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Optimum cost of generation for maximum loadability limit of power system using hybrid particle swarm optimization

Cited by 23 publications

References 7 publications

Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

Application of improved PSO to power transmission congestion management optimization model

Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization

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