Dynamic Security Border Identification Using Enhanced Particle Swarm Optimization

Kassabalidis, I.; El-Sharkawi, M.A.; Moulin, L. S.; Silva, A. P. Alves da

doi:10.1109/mper.2002.4312295

Cited by 20 publications

(36 citation statements)

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“…Weigth inertia is introduced to balance ability between global and local search [10]. Clerc introduced another parameter called Constriction Factor Approach (CFA) used to modify the algorithm of IPSO called Improved Modified Particle Swarm Optimization (MIPSO).…”

Section: Modified Improved Particle Swarm Optimizationmentioning

confidence: 99%

“…Equation modification velocity at each particle by using a constriction factor can be expressed as [8], [10], [12]: In general, researchers applied the constriction factor PSO algorithm by setting the value of c 1 and c 2 = 2.05 so that the obtained value of C = 0729. Algebraically equivalent to this value when using the inertia weight w = 0729 and c 1 = c 2 = 1.49445 [12].…”

Section: Modified Improved Particle Swarm Optimizationmentioning

confidence: 99%

“…MIPSO algorithm with Constricttion Factor Approach (CFA) can produce good solutions in comparison with algorithms that use ipso Inertia Weight Approach (IWA), although the CFA only consider the dynamic behavior of the particle or agent and the influence of particle interaction, where the equations has been developed with the best position to Pbest and Gbest, it may change during the search on the basis of PSO equation [10].…”

Section: Modified Improved Particle Swarm Optimizationmentioning

confidence: 99%

“…MIPSO not only improves the existing algorithms IPSO but also provides a new strategy to find a global solution which is better than the standard PSO algorithm by applying the chaotic sequence parameters constriction factor [8], [10]. MIPSO algorithm process can be described in the figure below: …”

Section: Implementation Of the Economic Dispatch Mipsomentioning

confidence: 99%

“…al. [10], has described the identification of safety limits on power systems by using the Enhanced Particle Swarm Optimization. M.A.…”

Section: Introductionmentioning

confidence: 99%

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Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

Ilyas

Rahman

2012

TELKOMNIKA

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AbstrakBiaya bahan bakar generator termal merupakan fungsi bebannya sendiri. Dalam Kata kunci: MIPSO, CFA, Opereasi Ekonomis (ED) Abstract Fuel cost of a thermal generator is its own load functions. In this research, Modified Improved Particle Swarm Optimization (MIPSO) is applied to calculate economic dispatch. Constriction Factor Approach (CFA) is used to modify IPSO algorithm because of the advantage to improve the ability of global searching and to avoid local minimum, so that the time needed to converge become faster.Simulation results achieved by using MIPSO method at the time of peak load of of 9602 MW, obtained generation cost is Rp 7,366,912,798,34 per hour, while generation cost of real system is Rp. 7,724,012,070.30

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Section: Modified Improved Particle Swarm Optimizationmentioning

confidence: 99%

Section: Modified Improved Particle Swarm Optimizationmentioning

confidence: 99%

Section: Modified Improved Particle Swarm Optimizationmentioning

confidence: 99%

Section: Implementation Of the Economic Dispatch Mipsomentioning

confidence: 99%

“…al. [10], has described the identification of safety limits on power systems by using the Enhanced Particle Swarm Optimization. M.A.…”

Section: Introductionmentioning

confidence: 99%

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Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

Ilyas

Rahman

2012

TELKOMNIKA

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Nonlinear multi-variable optimization of layered composites with nontraditional interfaces

Shabana

Elsawaf

2015

Struct Multidisc Optim

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This article deals with a nonlinear multi-variable optimization problem to minimize the critical thermal stress induced in a layered composite plate. The numerical model is solved by using the finite element analysis while the optimization is done by applying the particle swarm optimization technique. The plate contains nontraditional interfaces between the layers while their profiles follow a power law. Different parameters are optimized either individually or in combination by applying single and multi-variable optimizations so as to minimize the critical induced thermal stress in the structure. These optimized parameters include the interface profile parameter, the height of the nontraditional interface on the right surface relative to the mid-surface, and the thicknesses of the layers of the plate. It is found that the critical stress can be minimized greatly when tailoring the geometrical and interface parameters by using their optimum values.

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Constricted Local-Neighborhood Particle Swarm Optimization with Passive Congregation Applied in Reactive Power and Voltage Control

Vlachogiannis¹

2006

Electric Power Components and Systems

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In this study a hybrid particle swarm optimization (PSO) algorithm is proposed, called CLONEPAC-PSO. Specifically, the local-neighborhood variant of PSO is enhanced with the constriction factor approach and a new operator, reflecting the physical force of passive congregation, appeared in swarms. Passive congregation is an attraction of a particle to other group members where there is no display of social behavior. The results obtained by CLONEPAC-PSO in the reactive power and voltage control of an IEEE 30-bus test system are compared with those given by the global variant PSO algorithm based on passive congregation and another evolutionary algorithm, demonstrating the superiority of CLONEPAC-PSO.

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Dynamic Security Border Identification Using Enhanced Particle Swarm Optimization

Cited by 20 publications

References 0 publications

Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

Economic Dispatch Thermal Generator Using Modified Improved Particle Swarm Optimization

Nonlinear multi-variable optimization of layered composites with nontraditional interfaces

Constricted Local-Neighborhood Particle Swarm Optimization with Passive Congregation Applied in Reactive Power and Voltage Control

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