Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme

Ho, Pei Yee; Shimizu, Kazuyuki

doi:10.1016/j.ins.2007.01.011

Cited by 58 publications

(32 citation statements)

References 25 publications

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“…This three factors or their various combinations are usually employed as quality metric in constrained optimization literature (Ho & Shimizu, 2007;Mezura-Montes & Coello Coello, 2011). …”

Section: 1mentioning

confidence: 99%

“…Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. Ant colony optimization (ACO), particle swarm optimization (PSO) (J J Liang, Zhigang, & Zhihui, 2010), artificial immune systems (Farmer, Packard, & Perelson, 1986), evolutionary algorithms (EA) (Ho & Shimizu, 2007), artificial bee colony (ABC) (Karaboga & Akay, 2011), estimation of distribution algorithms (EDA) (Bi & Zhang, 2011;Larrañaga & Lozano, 2002) are just a few of them to be mentioned.…”

Section: Introductionmentioning

confidence: 99%

“…In the design of new algorithms most researchers have been focused to determine how to generate feasible individuals while maintaining a reasonable ratio between feasible and infeasible members in a population so that the algorithm is able to jump in a sparse feasible domain (Hamida & Schoenauer, 2002;Ho & Shimizu, 2007;Runarsson, 2000). Namely, all algorithms aim to find a balance between exploration-, exploitation-power, and feasibility.…”

Section: Introductionmentioning

confidence: 99%

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Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Kuşakçı

Can

2012

scjournal

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Abstract-( Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COPs.)

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“…This three factors or their various combinations are usually employed as quality metric in constrained optimization literature (Ho & Shimizu, 2007;Mezura-Montes & Coello Coello, 2011). …”

Section: 1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Kuşakçı

Can

2012

scjournal

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“…In order to assess the performance of LGP, some experiments were performed using some well known bi-objective and three-objective test functions [17,31], which are adapted from [9,16]. These test functions were also used by the authors of ParEGO [19] and NSGA II [8], which are well known in the computational intelligence community as very efficient techniques for multiobjective optimization.…”

Section: Experiments and Comparisonsmentioning

confidence: 99%

“…These include: Compromise Programming [3], Physical Programming [21][22][23][24][25][26][27], Normal Boundary Intersection (NBI) [4][5][6][7], and the Normal Constraint (NC) [28,29] methods. There is also a huge amount of work reported on population-based metaheuristics for MOP [1,2,[10][11][12]16,34,[37][38][39][40][41]. Comprehensive surveys can be found in [18,30,36].…”

Section: Introductionmentioning

confidence: 99%

Approximating Pareto frontier using a hybrid line search approach

Groşan

Abraham

2010

Information Sciences

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a b s t r a c tThe aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150 ms for the first stage and about 20 ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.

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Design optimization using multiple dominance relations

Cook

Willcox

Jarrett

2020

Numerical Meth Engineering

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A challenge in engineering design is to choose suitable objectives and constraints from many quantities of interest, while ensuring an optimization is both meaningful and computationally tractable. We propose an optimization formulation that can take account of more quantities of interest than existing formulations, without reducing the tractability of the problem. This formulation searches for designs that are optimal with respect to a binary relation within the set of designs that are optimal with respect to another binary relation. We then propose a method of finding such designs in a single optimization by defining an overall ranking function to use in optimizers, reducing the cost required to solve this formulation. In a design under uncertainty problem, our method obtains the most robust design that is not stochastically dominated faster than a multiobjective optimization. In a car suspension design problem, our method obtains superior designs according to a k-optimality condition than previously suggested multiobjective approaches to this problem. In an airfoil design problem, our method obtains designs closer to the true lift/drag Pareto front using the same computational budget as a multiobjective optimization. K E Y W O R D Saerodynamics, engineering design, optimization, probabilistic methods, shape design, suspension design, transonic *A binary relation is a comparison of two designs that determines whether one is better than the other, and for an optimal design no better design exists with respect to the relation. 3 Int J Numer Methods Eng. 2020;121:2481-2502.wileyonlinelibrary.com/journal/nme

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Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme

Cited by 58 publications

References 25 publications

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Approximating Pareto frontier using a hybrid line search approach

Design optimization using multiple dominance relations

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