Improving real-parameter genetic algorithm with simulated annealing for engineering problems

Hwang, Shun‐Fa; He, Rong-Song

doi:10.1016/j.advengsoft.2005.08.002

Cited by 118 publications

(35 citation statements)

References 13 publications

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“…As mentioned in Sect. 1, Hwang and He (2006) proposed a hybrid algorithm that merges RGA with SA. In their method after general GA operators, a new solution is randomly generated from neighborhood of the original solution.…”

Section: Improved Gamentioning

confidence: 99%

“…Another possibility is to merge GA with other powerful methods, such as simulated annealing (SA). For example, Hwang and He (2006) proposed a hybrid algorithm that merges real-parameter genetic algorithm (RGA) with SA. In their method after general GA operators, a new solution is randomly generated from the neighborhood of the original solution.…”

Section: Introductionmentioning

confidence: 99%

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An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Wang¹,

Okazaki²

2006

Soft Comput

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The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

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Section: Improved Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Wang¹,

Okazaki²

2006

Soft Comput

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“…The optimization method employed here is an improved genetic algorithm (GA). GA has been quite popular and has been applied to a variety of engineering problems [10]- [13].…”

Section: Optimization Methodsmentioning

confidence: 99%

Dynamic Optimization of Structures Subjected to Earthquake

Lavaei¹,

Lohrasbi²

2016

IJET

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Abstract-To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method.

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“…At present, most theories of genetic algorithms are concerned with binary coding, but binary coding may cause conflict easily between computation precision and computation efficiency and cannot effectively realize local search. Real coding (Balland, Estel, Cosmao, & Mouhab, 2000;Dhiranuch & Sun, 2005;Hwang & He, 2006a, 2006bRipon, Kwong, & Man, 2007;She, 2000) can greatly overcome the above deficiencies, and the main characteristics are as follows: (1) real numbers are directly used as chromosomes for genetic operation, and the specific coding and decoding processes are not necessary; all of which can reduce the complexity of the algorithm and improve the efficiency of the algorithm; (2) ''Hamming cliff'' existing in binary coding is eliminated and the local search power of the algorithm is improved;…”

Section: Introductionmentioning

confidence: 99%

Intelligent bionic genetic algorithm (IB-GA) and its convergence

Jin

et al. 2011

Expert Systems with Applications

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Improving real-parameter genetic algorithm with simulated annealing for engineering problems

Cited by 118 publications

References 13 publications

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Dynamic Optimization of Structures Subjected to Earthquake

Intelligent bionic genetic algorithm (IB-GA) and its convergence

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