An Experimental Study of Benchmarking Functions for Genetic Algorithms

Digalakis, Jason G.; Margaritis, Konstantinos G.

doi:10.1080/00207160210939

Cited by 147 publications

(33 citation statements)

References 9 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As in Ackley function, the optima of Griewank function are regularly distributed. Penalized functions are difficult due to the combinations of different periods of the sine function (Digalakis and Margaritis 2002;Boyer et al 2005).…”

Section: Experiments 1: Large-scale Unconstrained Optimizationmentioning

confidence: 98%

See 1 more Smart Citation

Artificial bee colony algorithm for large-scale problems and engineering design optimization

2010

View full text Add to dashboard Cite

Engineering design problems are generally large scale or nonlinear or constrained optimization problems. The Artificial Bee Colony (ABC) algorithm is a successful tool for optimizing unconstrained problems. In this work, the ABC algorithm is used to solve large scale optimization problems, and it is applied to engineering design problems by extending the basic ABC algorithm simply by adding a constraint handling technique into the selection step of the ABC algorithm in order to prefer the feasible regions of entire search space. Nine well-known large scale unconstrained test problems and five well-known constrained engineering problems are solved by using the ABC algorithm and the performance of ABC algorithm is compared against those of state-of-the-art algorithms.

show abstract

Section: Experiments 1: Large-scale Unconstrained Optimizationmentioning

confidence: 98%

“…Flat surfaces are obstacles for optimization algorithms which do not have variable step sizes, because they do not give any information as to which direction is favourable (Digalakis and Margaritis 2002). The surface of Schwefel function is composed of a great number of peaks and valleys.…”

Section: Experiments 1: Large-scale Unconstrained Optimizationmentioning

confidence: 99%

Artificial bee colony algorithm for large-scale problems and engineering design optimization

2010

View full text Add to dashboard Cite

show abstract

“…It is well known that heuristic algorithms based on computational or swarm intelligence are successfully performed to determine the coefficients of the optimization problems more than the analytical solution techniques. Therefore, the GA, which is the heuristic algorithm, is utilized to predict the coefficients of the PID controller and SMC in this study [24,25].…”

Section: Gamentioning

confidence: 99%

“…It is a kind of stochastic optimization algorithm that is used in order to find the global or local optimum of a function [24]. This function may include more than one variable.…”

Section: Introductionmentioning

confidence: 99%

Control of SVC based on the sliding mode control method

Köse¹,

Kizmaz²,

Abacı³

et al. 2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:A genetic algorithm (GA)-based sliding mode controller is proposed to improve the voltage stability of a power system with a static var compensator. The proposed controller is examined for improving the load bus voltage, which changes under different demanding powers, and its performance for transient analysis is compared with the ZieglerNichols proportional-integral (ZNPI), Lyapunov-based sliding mode control (LASMC), and GA-based proportionalintegral-derivative (GAPID) controllers. The dynamic equations, consisting of a 2-bus nonlinear system, are converted to a mathematical description of sliding mode techniques. The optimum values of the sliding mode controller and proportional-integral-derivative (PID) coefficients that are required are calculated using the GA technique. Output voltage performances are obtained based on the demanding powers, which are at a constant variation. In this process, sliding mode, ZNPI, GAPID, and LASMC controllers are preferred in order to control the system. The results show that the GA sliding mode controller method is more effective than the ZNPI, GAPID, and LASMC controllers in voltage stability enhancement.

show abstract

“…In real world applications, the faster computation of accurate results is the ultimate aim. In recent time, a new optimization technique called Teaching learning based optimization [1] is gaining popularity [2][3][4][5][6][7][8] due to its ability to achieve better results in comparatively faster convergence time to techniques like Genetic Algorithms (GA) [9,10], Particle swarm Optimizations (PSO) [11][12][13][14][15][16][17] Differential Evolution (DE) [18][19][20] and some of its variants like DE with Time Varying Scale Factor (DET-VSF), DE with Random Scale Factor (DERSF) [21] etc. The main reason for TLBO being faster to all other contemporary evolutionary techniques is it has no parameters to tune.…”

Section: Introductionmentioning

confidence: 99%

Weighted Teaching-Learning-Based Optimization for Global Function Optimization

Satapathy¹,

Naik²,

Parvathi³

2013

View full text Add to dashboard Cite

Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.

show abstract

An Experimental Study of Benchmarking Functions for Genetic Algorithms

Cited by 147 publications

References 9 publications

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Control of SVC based on the sliding mode control method

Weighted Teaching-Learning-Based Optimization for Global Function Optimization

Contact Info

Product

Resources

About