Handling constraints using multiobjective optimization concepts

Aguirre, Arturo Hernández; Rionda, Salvador Botello; Coello, Carlos A. Coello; Lizárraga, Giovanni; Mezura-Montes, Efrén

doi:10.1002/nme.947

Cited by 143 publications

(55 citation statements)

References 18 publications

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“…Reference [20] gives a good survey of these strategies. Another approach, based on the definition of constrained dominance is developed by Deb et al [8].…”

Section: Constraint Handling Constraints Can Be Classified Into Two mentioning

confidence: 99%

Multiobjective Optimization by a Modified Artificial Immune System Algorithm

Freschi

Repetto

2005

Lecture Notes in Computer Science

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Abstract. The aim of this work is to propose and validate a new multiobjective optimization algorithm based on the emulation of the immune system behavior. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multiobjective evolutionary algorithms described in literature. The proposed approach is compared with the NSGA2 algorithm, that is representative of the state-of-the-art in multiobjective optimization. Algorithms are tested versus three standard problems (unconstrained and constrained), and comparisons are carried out using three different metrics. Results show that the proposed approach have performances similar or better than those produced by NSGA2, and it can become a valid alternative to standard algorithms.

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“…Reference [20] gives a good survey of these strategies. Another approach, based on the definition of constrained dominance is developed by Deb et al [8].…”

Section: Constraint Handling Constraints Can Be Classified Into Two mentioning

confidence: 99%

Multiobjective Optimization by a Modified Artificial Immune System Algorithm

Freschi

Repetto

2005

Lecture Notes in Computer Science

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“…As shown in Tables III and IV, Number of trials from [20,23] to make a fair comparison. Furthermore, ATM is extended to yield more experimental results using the additional test functions adopted in this paper, i.e.…”

Section: Comparison With the Atm And The Is-paesmentioning

confidence: 99%

“…An infeasible solution archiving and replacement mechanism is also introduced in this method. [19], and the method (abbreviated as IS-PAES) [20] based on the Pareto archived evolutionary strategy (ES) [21]. Mezura-Montes and Coello Coello [22] presented an extensive survey of the constraint-handling technique based on evolutionary multi-objective optimization concepts.…”

Section: Definition 3 (Pareto Optimal Set)mentioning

confidence: 99%

Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization

Wang

Cai

Zhou

2008

Numerical Meth Engineering

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SUMMARYAdaptive trade-off model (ATM) is a constraint-handling mechanism proposed recently. The main advantages of this model are its simplicity and adaptation. Moreover, it can be easily embedded into evolutionary algorithms for solving constrained optimization problems. This paper proposes a novel method for constrained optimization, which aims at accelerating the ATM using shrinking space technique. Eighteen benchmark test functions and five engineering design problems are used to test the performance of the method proposed. Experimental results suggest that combining the ATM with the shrinking space technique is very beneficial. The method proposed can promptly converge to competitive results without loss of the quality and the precision of the final results. Performance comparisons with some other state-of-the-art approaches from the literature are also presented.

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“…With the development of evolutionary computation, some representative evolutionary algorithms only suitable for either single or multi-objective optimization problems have been proposed in the literature [1][2][3][4][5][6]. Nevertheless, in practice, because of different requirements and goals, many optimization problems, e.g., university timetable, engineering design, flow-shop scheduling, and so on, frequently change their type of optimization; in other words, they sometimes include only one objective within some time span but multiple conflicting objectives within another time span.…”

Section: Introductionmentioning

confidence: 99%

Artificial immune optimization system solving constrained omni-optimization

Zhang

2011

Evol. Intel.

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This work investigates an artificial immune optimization system suitable for single and multi-objective constrained optimization. In this optimizer, an evaluation index, which can decide the importance of individual in the current population, is developed to accelerate population division; the niching-like proliferation scheme is introduced to strengthen the diversity of population. Thereafter, those diverse antibodies, with the help of immune evolution operations, evolve their structures along different directions. Theoretical results show that such optimization system is convergent with low computational complexity. Experimentally, one such optimizer is sufficiently examined by a suite of single and multi-objective test problems. Comparative experiments illustrate that the optimizer with some striking characteristics is a potentially alternative optimization tool for constrained omni-optimization.

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Handling constraints using multiobjective optimization concepts

Cited by 143 publications

References 18 publications

Multiobjective Optimization by a Modified Artificial Immune System Algorithm

Multiobjective Optimization by a Modified Artificial Immune System Algorithm

Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization

Artificial immune optimization system solving constrained omni-optimization

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