Genetic Algorithm and Hybrid Genetic Algorithm for Space Allocation Problems - A Review

Sharma, Jyoti; Singhal, Reena

doi:10.5120/16585-6283

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…In the recent few years, several modications have been made in the of the original framework of the genetic algorithm (GA) aiming at to alleviate their drawbacks. GA has successfully tackled dierent real-world problems such as space allocation problems on dierent sample test like warehouse, shelf, building oors and container and many others [55]. Dierent benchmark functions with continuous and discrete variables are also solved by GAs with great success.…”

Section: Hybridization Of Genetic Algorithm With Bfgsmentioning

confidence: 99%

Hybrid Genetic Algorithms for Global Optimization Problems

Asim¹

2017

HJMS

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In the last two decades the eld evolutionary computation has become a mainstream and several types of evolutionary algorithms are developed for solving optimization and search problems. Evolutionary algorithms (EAs) are mainly inspired from the biological process of evolution. They do not demand for any concrete information such as continuity or dierentiability and other information related to the problems to be solved. Due to population based nature, EAs provide a set of solutions and share properties of adaptation through an iterative process. The steepest descent methods and Broyden-Fletcher-Goldfarb-Shanno (BFGS),Hill climbing local search are quite often used for exploitation purposes in order to improve the performance of the existing EAs. In this paper, We have employed the BFGS as an additional operator in the framework of Genetic Algorithm. The idea of add-in BFGS is to sharpen the search around local optima and to speeds up the search process of the suggested algorithm. We have used 24 benchmark functions which was designed for the special session of the 2005 IEEE-Congress on Evolutionary Computation (IEEE-CEC 06) to examine the performance of the suggested hybrid GA. The experimental results provided by HGBA are much competitive and promising as compared to the stand alone GA for dealing with most of the used test problems

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Section: Hybridization Of Genetic Algorithm With Bfgsmentioning

confidence: 99%

Hybrid Genetic Algorithms for Global Optimization Problems

Asim¹

2017

HJMS

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“…Space layout problems can be solved through the application of Genetic Algorithm (GA) and Hybrid Genetic Algorithm (HGA) as proposed by Jyoti Sharmaet. Al [9]. These type of algorithms become more suitable when the problem size is varied and when the problem is clearly defined.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between PSO and Genetic Algorithms and for Optimizing of Permanent Magnet Synchronous Generator (PMSG) Machine Design

Prashanth¹,

Sujatha²

2018

IJET

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This paper proposes application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the design of direct-driven permanent magnet synchronous generator machine (PMSGs) for wind turbine applications. The power rating of these machines is in the mega watt (MW) level. The constraints and requirements of the generator are outlined. The proposed design scheme optimizes various PMSG parameters like Pole pair number, Linear current density, Air gap thickness, Rotor outer diameter, Relative width of the permanent magnet etc to achieve certain objectives like maximizing efficiency, increasing Torque, improving power factor etc. The results obtained by GA algorithm and those by PSO algorithm are compared. The performance of Particle Swarm Optimization is found to be better than the Genetic Algorithm, as the PSO carries out global search and local searches simultaneously, whereas the Genetic Algorithm concentrates mainly on the global search. Results show that the proposed PSO optimization algorithm is easy to develop and apply and produced competitive designs compared to the GA algorithm.

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“…Interval optimization [13], branch-and-bound [27,55] and algebraic techniques [54] are commonly used deterministic methods.On the other hand, stochastic nature based algorithms evolve their set of solutions with randomness. Simulated annealing (SA) [23,22], Monte Carlo sampling [15], stochastic tunneling [32],parallel tempering [34], Genetic Algorithm (GA) [17], Evolutionary Strategies (ES) [43], Evolutionary Programming (EP) [20,21,7], Particle Swarm Optimization (PSO) [25,67], Ant Colony Optimization (ACO) [60], differential evolution (DE) [52], Krill herd algorithm based on cuckoo search [2,62],Elephant Herding Optimization (EHO) [8,58], Moth search algorithm [56], Monarch Butterfly Optimization (MBO) [57], Earthworm Optimization Algorithm (EWA) [59], Plant Prorogation Algorithm (PPA) [49,50,51,44] and hybrid EAs [28,47,18] are well-known stochastic methods. Evolutionary computation is the collective name used for population base evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

Constrained Optimization with Hybrid Evolutionary Algorithms

Khan¹,

Yeniay²,

Shah³

et al. 2018

HJMS

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Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers.Evolutionary algorithms are not directly applied on constrained optimization problems. However, different constraint-handling techniques are incorporated in their framework to adopt it for dealing with constrained environments. This paper suggests an hybrid constrained evolutionary algorithm (HCEA) that employs two penalty functions simultaneously. The suggested HCEA has two versions namely HCEA-static and HCEA-adaptive. The performance of the HCEA-static and HCEA-adaptive algorithms are examined upon the constrained benchmark functions that are recently designed for the special session of the 2006 IEEE Conference of Evolutionary Computation (IEEE-CEC'06). The experimental results of the suggested algorithms are much promising as compared to one of the recent constrained version of the JADE. The converging behaviour of the both suggested algorithms on each benchmark function is encouraging and promising in most cases.

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Genetic Algorithm and Hybrid Genetic Algorithm for Space Allocation Problems - A Review

Cited by 9 publications

References 17 publications

Hybrid Genetic Algorithms for Global Optimization Problems

Hybrid Genetic Algorithms for Global Optimization Problems

Comparison Between PSO and Genetic Algorithms and for Optimizing of Permanent Magnet Synchronous Generator (PMSG) Machine Design

Constrained Optimization with Hybrid Evolutionary Algorithms

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