Genetic algorithms in constrained optimization

Reid, Darryn J.

doi:10.1016/0895-7177(96)00014-3

Cited by 38 publications

(21 citation statements)

References 6 publications

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“…The basic concepts of GA have been described by the investigation carried out by Holland [60], who explained how to add intelligence to a program computing with the crossover exchange of genetic material and transfer as a source of diversity. Based on [60], a variety of GA versions are provided in literature for different problems [e.g., [49][50][51][52][53]. The simple GA which is compared with PHGA in this paper is defined as a GA with randomly generated chromosomes, 0 0 0 0 0 0 0 0 0 3 x 12 8 8 8 8 8 7 6 6 4 3 x 21 1 1 1 1 1 1 1 2 2 1 I 2 38 38 38 37 34 31 26 25 18 18 z 31 4 3 3 3 3 2 2 2 2 1 i 3 32 31 29 32 31 26 26 18 8 1 z 41 3 3 3 3 3 3 3 3 2 10) tournament selection [61], single point crossover [61], static mutation, rejection strategy for constraint handling [62], and without any local search method.…”

Section: Comparative Results Of Phga and Simple Gamentioning

confidence: 99%

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A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

Mohtashami

2014

Int J Adv Manuf Technol

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This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer nonlinear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of nondominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA.

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Section: Comparative Results Of Phga and Simple Gamentioning

confidence: 99%

“…Genetic algorithms have proven to be very adaptable to a great variety of different complex optimization tasks, and many researches use this algorithm to solve different problems [e.g., [49][50][51][52][53]. Taking into account non-linear state for the first objective function, a hybrid genetic algorithm is proposed to solve the problem (15).…”

Section: Hgamentioning

confidence: 99%

A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

Mohtashami

2014

Int J Adv Manuf Technol

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“…However, only 30% out of total runs is converged solved by GA and AAE is of 3 10 − magnitude under the same conditions. Such results indicate that AGA outperforms GA in precision and convergence.…”

Section: A Analysis Of Convergence Probability Of Agamentioning

confidence: 90%

“…Genetic Algorithm (GA) [1][2][3] is a kind of intelligent algorithm based on biological evolution simulation. It had been widely applied in parameter optimization, pattern recognition, system control [4][5] because of its low requirements in application and ability of seeking global optimum.…”

Section: Introductionmentioning

confidence: 99%

Improvement on Boundary Searching of Accelerating Genetic Algorithm

Zhong

Lin

2012

2012 Second International Conference on Intelligent System Design and Engineering Application

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Accelerating Genetic Algorithm (AGA)'s disadvantages of unable to search the optimal solution when the solution is in the boundary of feasible region was proved through theoretical analysis and numerical experimentation. The solution of adding random individuals whose variable obeying to saddle distribution into initial population to increase the ability of searching the optimal solution in the boundary of AGA was proposed. The results of numerical tests show that the introduction of special individuals obeying to triangular distribution or pulse distribution can increase the accuracy by 3~10 times and convergence probability by 10%~20% when the global optimal solution is near the boundaries of feasible region and the accuracy is increased by 3~7 times and convergence probability by 25%~50% when global optimum is in the boundary of feasible region.

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“…The allele obtained by each of gene mutations distributes near the original value of the gene in larger probability density, and also has a certain probability density in far away from the original value of the gene [15]. (7) Evolutionary iteration.…”

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confidence: 99%