Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices

Chandrasekar, Karthik; Ramana, N. Venkata

doi:10.5370/jeet.2012.7.4.493

Cited by 33 publications

(18 citation statements)

References 26 publications

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“…one point crossover, which is normally used in the GA. However, the GA was outperformed by the differential evolution (DE) algorithm in terms of global search ability [44,45]. Li and Zhang [46], Li and Zhang [47] proposed a multi-objective differential evolution based decomposition (MODE/D) in which the genetic operator was replaced by the differential operators and revealed that the MODE/D performed better than several other MOEAs on many test problems.…”

Section: Amoea/d-dementioning

confidence: 99%

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Chutima¹,

Kirdphoksap²

2019

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The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in the Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergencerelated metrics.

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Section: Amoea/d-dementioning

confidence: 99%

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Chutima¹,

Kirdphoksap²

2019

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“…Finding the most optimum system configuration consider both a technical aspect and economical aspect, an optimization method or optimization algorithm is needed in search of the global optimum e.g. genetic algorithm (GA), particle swarm optimization (PSO) algorithm and differential evolution (DE) Algorithm [5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In [5][6] [8], which used genetic algorithms to size optimal PV/Wind/batteries hybrid systems by minimizing LPSP and the ACS. The studied showed genetic algorithms made possible to calculate the number of the components of the optimal configuration which ensure a cover of the load with an acceptance of an LPSP.…”

Section: Introductionmentioning

confidence: 99%

Sizing Optimization and Operational Strategy of Hres (PV-WT) using Differential Evolution Algorithm

Pakaya¹,

Has²,

Putra³

2018

EECSI

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The instability of energy resources and corresponding cost of the system are the main two problems for designing the hybrid solar-wind power generation systems. The configuration of the system must have a high reliability on the power supply availability but with a minimum cost. The purpose of this paper is to find the most optimum or balanced configuration between technical reliability and total annual cost for the PV module number, the wind turbine number, and the battery number. The appropriate strategy of load management is needed by adjusting the potential energy resource to the load power demand. Loss of Power Supply Probability (LPSP) is a method to determine the ratio of power generation unavailability by the system configuration which used as technical analysis. Annualized Cost of System (ACS) is a method to determine the total annualized cost of the project lifetime which used as economic analysis. The result from the simulation showed that the Differential Evolution (DE) algorithm can be an alternative method to find the best configuration with a low number of LPSP and ACS. Since DE has a better efficacy and faster time to find global optimum than other algorithms.

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“…Recent research suggests that meta-heuristic algorithms such GA, DE, and PSO produce consistently better results than conventional tuning techniques with DE outperforming the other algorithms in results and computational cost [3].Furthermore, Kachitvichyanukul [2], in a comparison of the three algorithms, concluded that GA falls into local minima with greater tendency than the other algorithms, while PSO tends to result in higher density of solutions within the solution space with DE being closely comparable to PSO.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of DE, PSO and GA for Position Domain PID Controller Tuning

Ouyang

Pano

2015

Algorithms

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Gain tuning is very important in order to obtain good performances for a given controller. Contour tracking performance is mainly determined by the selected control gains of a position domain PID controller. In this paper, three popular evolutionary algorithms are utilized to optimize the gains of a position domain PID controller for performance improvement of contour tracking of robotic manipulators. Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are used to determine the optimal gains of the position domain PID controller, and three distinct fitness functions are also used to quantify the contour tracking performance of each solution set. Simulation results show that DE features the highest performance indexes for both linear and nonlinear contour tracking, while PSO is quite efficient for linear contour tracking. Both algorithms performed consistently better than GA that featured premature convergence in all cases.

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Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices

Cited by 33 publications

References 26 publications

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

Sizing Optimization and Operational Strategy of Hres (PV-WT) using Differential Evolution Algorithm

Comparative Study of DE, PSO and GA for Position Domain PID Controller Tuning

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