Solving Constrained Multi-objective Optimization Problems Using Non-dominated Ranked Genetic Algorithm

Jadaan, Omar Al; Rao, C. Raghavendra; Rajamani, Lakshmi

doi:10.1109/ams.2009.38

Cited by 14 publications

(10 citation statements)

References 5 publications

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“…Jadaan and Rao [35] combined a new non-dominated ranked genetic algorithm (NRGA) with a non-parameter penalty method to continuously search for the better Pareto optimal set. Zhang et al [36] designed a multi-hive colony artificial bee colony method to deal with CMOPs, by employing a general coevolution model.…”

Section: A Methods Based On Penalty Functionmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

165

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Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization.

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Section: A Methods Based On Penalty Functionmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

165

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“…The selection process selects chromosomes from the mating pool directed by the survival of the fittest concept of natural genetic systems [22], [23]. In the proportional selection method (Rank Roulette Wheel selection (RRWS) [24]) is adopted in this paper, a string is assigned a number of copies, which is proportional to its fitness in the population, that send into the mating pool for further genetic operations.…”

Section: ) Selectionmentioning

confidence: 99%

Enhancing Data Selection Using Genetic Algorithm

Jadaan

Abdulal

Hameed

et al. 2010

2010 International Conference on Computational Intelligence and Communication Networks

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Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and selecting replica with multiple selection criteria -availability, security and time-is one of these problems. In this paper, a rank based elitist clustering Genetic Algorithm is proposed named RRWSGA, which alleviates the problem of being trapped in local clustering centroids using -mean. Simulation results show that the proposed RRWSGA, outperforms -mean by 9%. Much better performance of RRWSGA is observed.

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“…In our experiments, we use as reference set the obtained points from our 1-MOEA approach. This first quantification is evaluated through equation (18) (Tan et al, 2001;Deb and Jain, 2002;Al Jadaan et al, 2009;Chang et al, 2005) which gives the measure of the minimum Euclidean distance to the set P * . …”

Section: Multi-objective Genetic Algorithmsmentioning

confidence: 99%

“…In this metrics as the value of ESS is lower, the approximation is better (Tan et al, 2001;Deb and Jain, 2002;Al Jadaan et al, 2009;Chang et al, 2005), and it is defined as: where d i is the minimum distance between two solutions in the population P(t), j ¼ 1, 2, 3, . .…”

Section: Multi-objective Genetic Algorithmsmentioning

confidence: 99%

“…For optimizing conflicting trade off objectives we use Pareto-optimality-based algorithms, as the recently developed non-dominated sorting genetic algorithm (NSGA-II) (Ishibuchi et al, 2009;Jian-Guo, 2009;Dias and de Vasconcelos, 2002;Vasconcelos et al, 2005;Yao and Harley, 2010;Al Jadaan et al, 2009). In this paper, a large set of Pareto optimal solutions are obtained against a unique and definitive optimum result -one point of design space is represented as the pair piezoresistance value and sensitivity.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective genetic algorithms applied to low power pressure microsensor design

Monzón-Verona

García-Alonso

Sosa

et al. 2013

Engineering Computations

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Purpose -The purpose of this paper is to explain in detail the optimization of the sensitivity versus the power consumption of a pressure microsensor using multi-objective genetic algorithms. Design/methodology/approach -The tradeoff between sensitivity and power consumption is analyzed and the Pareto frontier is identified by using NSGA-II, AMGA-II and 1-MOEA methods. Findings -Comparison results demonstrate that NSGA-II provides optimal solutions over the entire design space for spread metric analysis, and AMGA-II is better for convergence metric analysis. Originality/value -This paper provides a new multiobjective optimization tool for the designers of low power pressure microsensors.

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Solving Constrained Multi-objective Optimization Problems Using Non-dominated Ranked Genetic Algorithm

Cited by 14 publications

References 5 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

Enhancing Data Selection Using Genetic Algorithm

Multi-objective genetic algorithms applied to low power pressure microsensor design

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