Genetic Algorithm: Review and Application

Kumar, Manoj; Husian, Mohammad; Upreti, Naveen; Gupta, Deepti

doi:10.2139/ssrn.3529843

Cited by 243 publications

(162 citation statements)

References 9 publications

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“…Individuals with less values have a high probability of being selected whereas the new generation of individuals may have a minor objective value than the previous generation. The evolution process is repeated until the stopping criterion is satisfied [2].…”

Section: Genetic Methodsmentioning

confidence: 99%

Genetic algorithm and particle swarm optimization combined with Powell method

Bento¹,

Pinho²,

Pereira³

et al. 2013

AIP Conference Proceedings

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In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm -Genetic Algorithm and a bio-inspired hybrid algorithm -Particle Swarm Optimization, both combined with the local method -Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

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Section: Genetic Methodsmentioning

confidence: 99%

Genetic algorithm and particle swarm optimization combined with Powell method

Bento¹,

Pinho²,

Pereira³

et al. 2013

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…Genetic algorithm emulate the process of natural selection and genetic mechanism in Darwin's theory of evolution, and seek the optimal solution through a natural genetic process 54 . The genetic algorithm starts with an initial population (a set of potential solutions), which consists of a number of individuals with combinations of different genes.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Enhanced stability of flows through contraction channels: Combining shape optimization and linear stability analysis

et al. 2019

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The first flow bifurcation, in channels with a sudden geometry contraction, is controlled through shape optimisation to delay the onset of asymmetry. First, we confirm the existence of a pitchfork type bifurcation instability, already reported in similar geometries. The global mode responsible for this bifurcation leads to asymmetric flow for Reynolds numbers beyond a critical value. Second, we propose a global shape optimisation methodology to introduce small modifications in the channel geometry that lead to flows with enhanced stability. Our results include three contraction ratios C = 2, 4 and 8, where C is the ratio of upstream to downstream channel widths.The shape optimisation aims at minimising the growth rate of the unstable mode responsible for asymmetry. Sensitivity analysis is used to find an appropriate geometry parametrisation, which is defined through super-elliptic curves, and limited to small deformations. Additionally, a dynamically updated surrogate model (based on Radial Basis Functions) is developed to drive the optimisation. This substitutes expensive function evaluations, each requiring the solution of a steady Navier-Stokes base flow computation and a solution of an eigenvalue problem (linear stability analysis). Finally, a mode tracking algorithm identifies the eigenmode responsible for the onset of asymmetry during the optimisation.The optimised geometries show rounded corners and are stable for Reynolds numbers well beyond the original values. For all contraction ratios, the critical Reynolds number increases by at least 7.9 times with respect to the original values. three-dimensional simulations confirm that the optimised geometry is more stable than the original when periodic boundaries are used on the side walls. When comparing the drag of the optimised geometry to the original, we obtain a reduction of at least 64%.

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“…The GA can be applied to solve a variety of optimization problems, including problems in which the objective function is discontinuous, non-differentiable, stochastic, or highly nonlinear [9].…”

Section: Optimization Proceduresmentioning

confidence: 99%

A Genetic Algorithm Approach for the Scheduling in a Robotic-Centric Flexible Manufacturing System

Pereira

Ferreira

Barbosa

et al. 2017

Human-Centric Robotics

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Scheduling assumes a crucial importance in manufacturing systems, optimizing the allocation of operations to the right resources at the most appropriate time. Particularly in the Flexible Manufacturing System (FMS) topology, where the combination of possibilities for this association exponential increases, the scheduling task is even more critical. This paper presents a heuristic scheduling method based on genetic algorithm for a robotic-centric FMS. Real experiments show the effectiveness of the proposed algorithm, ensuring a reliable and optimized scheduling process.

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Genetic Algorithm: Review and Application

Cited by 243 publications

References 9 publications

Genetic algorithm and particle swarm optimization combined with Powell method

Genetic algorithm and particle swarm optimization combined with Powell method

Enhanced stability of flows through contraction channels: Combining shape optimization and linear stability analysis

A Genetic Algorithm Approach for the Scheduling in a Robotic-Centric Flexible Manufacturing System

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