Hybrid genetic algorithm for the open capacitated arc routing problem

Arakaki, Rafael Kendy; Usberti, Fábio Luiz

doi:10.1016/j.cor.2017.09.020

Cited by 45 publications

(23 citation statements)

References 19 publications

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“…[ 119 ]. To improve the performance in the above-mentioned areas of scheduling, researchers developed various genetic representation [ 12 , 159 , 215 ], genetic operators, and hybridized GA with other methods [ 2 , 67 , 147 , 219 ].…”

Section: Applicationsmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,700

761

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In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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Section: Applicationsmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,700

761

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“…Kim et al (2007) combined a genetic algorithm with a particle swarm optimization tool in the run-time for searching the global optimum of multimodal functions. Arakaki and Usberti (2018) and Usberti et al (2018) Hooke and Jeeves algorithm is a local search optimizer for optimization problems which are not necessarily differentiable. Algorithm starts searching using an initial solution.…”

Section: Algorithm 2 Machine Coded Compact Genetic Algorithmmentioning

confidence: 99%

Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems

Satman

Akadal²

2020

Alphanumeric Journal

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In this paper, we extend the Compact Genetic Algorithm (CGA) for real-valued optimization problems by dividing the total search process into three stages. In the first stage, an initial vector of probabilities is generated. The initial vector contains the probabilities of bits having 1 depending on the bit locations as defined in the IEEE-754 standard. In the second stage, a CGA search is applied on the objective function using the same encoding scheme. In the last stage, a local search is applied using the result obtained by the previous stage as the starting point. A simulation study is performed on a set of well-known test functions to measure the performance differences. Simulation results show that the improvement in search capabilities is significant for many test functions in many dimensions and different levels of difficulty.

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“…In 2002 this algorithm was extended to treat ECARP (Extended CARP), SCARP (Stochastic CARP) and PCARP (Periodic CARP) by Wahiba Ramdane Cherif [9]. In 2018, Arakaki et al proposed a hybrid genetic algorithm to solve the problem of open CARP [10].…”

Section: Metaheuristicsmentioning

confidence: 99%