A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems

Ameca-Alducin, Maria-Yaneli; Hasani-Shoreh, Maryam; Blaikie, Wilson; Neumann, Franz‐Josef; Mezura-Montes, Efrén

doi:10.48550/arxiv.1802.05825

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“…In this section differential evolution algorithm, the applied constraint handling techniques and change detection mechanism are briefly introduced. Differential evolution (DE) is a stochastic search algorithm that is simple, reliable and fast and showed competitive results in constraint and dynamic optimization [15]. Each vector x i,G in the current population (called at the moment of the reproduction as target vector) generates one trial vector u i,G by using a mutant vector v i,G .…”

Section: Differential Evolution Algorithm For Dynamic Constrained Opt...mentioning

confidence: 99%

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On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints

Hasani-Shoreh

Ameca-Alducin

Blaikie

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.

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Section: Differential Evolution Algorithm For Dynamic Constrained Opt...mentioning

confidence: 99%

“…In addition, some papers enhanced both constraint handling and dynamic handling mechanisms [5]. Among the many evolutionary algorithms, DE has showed competitive results in dynamic and constrained optimization problems so far [15].…”

Section: Introductionmentioning

confidence: 99%