Multi-objective test problems, linkages, and evolutionary methodologies

Deb, Kalyanmoy; Sinha, Ankur; Kukkonen, Saku

doi:10.1145/1143997.1144179

Cited by 110 publications

(62 citation statements)

References 15 publications

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“…Hence, when the functions are non-differentiable, another optimization method should have been used. The five objective problems are rotated functions and the SBX recombination operator, which is a variablewise recombination operator, does not perform well in such problems [16] and we suspect this to be a reason for high IGD values on such problems. The degraded performance on some of the constrained problems may be due to the constraint handling strategy, which is argued [17] to have problems to maintain diversity.…”

Section: A Simulation Resultsmentioning

confidence: 99%

Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Sindhya¹,

Sinha²,

Deb³

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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Abstract-Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. Recently, a lot of emphasis have been laid on hybridizing evolutionary algorithms with MCDM and mathematical programming algorithms to yield a computationally efficient and convergent procedure. In this paper, we test an augmented local search based EMO procedure rigorously on a test suite of constrained and unconstrained multiobjective optimization problems. The success of our approach on most of the test problems not only provides confidence but also stresses the importance of hybrid evolutionary algorithms in solving multi-objective optimization problems.

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Section: A Simulation Resultsmentioning

confidence: 99%

Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Sindhya¹,

Sinha²,

Deb³

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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“…More recently, Li and Zhang [2006] and Deb et al [2006] presented MOOPs with decision variable dependencies (or linkages). Zhou et al [2007] modified FDA1 to incorporate dependencies between the decision variables.…”

Section: U(t) and V(t) Functions Of Time T The Selection Of U(t) Andmentioning

confidence: 99%

Benchmarks for dynamic multi-objective optimisation algorithms

Helbig

Engelbrecht

2014

ACM Comput. Surv.

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Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.

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“…In PCX based NSGA-II procedure, we choose three solutions using the binary tournament selection operator from the parent population. Each of them is, in turn, used as the index parent and an offspring solution is created by applying PCX operator to three chosen solutions [4].…”

Section: A Elitist Non-dominated Sorting Genetic Algorithmmentioning

confidence: 99%

“…This recombination operator has shown an efficient way of solving real-parameter optimization problems. NSGA-II [2], [3] with PCX recombination operator was introduced and tested successfully on various multi-objective and epistatic test problems [4]. For locally improving the obtained set of non-dominated solutions of PCX-NSGA-II, a classical optimization method such as SQP is applied in this paper.…”

Section: Introductionmentioning

confidence: 99%

A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming

Kumar

Sharma

Deb

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Despite the existence of a number of procedures for multi-objective optimization using evolutionary algorithms, there is still the need for a systematic and unbiased comparison of different approaches on a carefully chosen set of test problems.In this paper, a hybrid approach using PCX based NSGA-II and Sequential Quadratic Programming (SQP) is applied on 19 benchmark test problems consisting of two, three and five objectives. PCX-NSGA-II is used as a population based algorithm where SQP is used as a local search procedure. A population based approach helps in finding the non-dominated set of solutions with a good spread, whereas SQP improves the obtained set of non-dominated solutions locally. The results obtained by the present approach shows mixed performance on the chosen test problems.

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Multi-objective test problems, linkages, and evolutionary methodologies

Cited by 110 publications

References 15 publications

Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems

Benchmarks for dynamic multi-objective optimisation algorithms

A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming

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