A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaining close to optimal solutions. Using a multi-objective approach, we evolve constrained continuous problems having a set of linear and/or quadratic constraints where the different evolutionary approaches show a significant difference in performance. Afterwards, we discuss the features of the constraints that exhibit a difference in performance of the different evolutionary approaches under consideration.Comment: 16 Pagesm 2 Figure

An Analysis of Differential Evolution Parameters on Rotated Bi-objective Optimization Functions

International audienceDifferential evolution (DE) is a very powerful and simple algorithm for single- and multi-objective continuous optimization prob- lems. However, its success is highly affected by the right choice of param- eters. Although significant progress has been made in the single-objective realm, the choice of competitive DE parameters for multi-objective prob- lems is still far from being well understood. In particular, authors of suc- cessful multi-objective DE algorithms usually use parameters which do not render the algorithm invariant with respect to rotation of the coor- dinate axes in the decision space. In this work we explore what are the consequences of using such parameters when the problem rotates. and try to establish which parameters offer the more robust setting with respect to rotation invariance. We do this by testing a DE algorithm with various parameters on a testbed of bi-objective problems with various modality and separability characteristics. Then we explore how the performance changes when we rotate the axes in a controlled manner. We find out that our results are consistent with the single-objective theory but only for unimodal problems. On multi-modal problems, surprisingly, param- eter settings which do not render the algorithm rotationally invariant have a consistently good performance for all studied rotations

A feature-based comparison of evolutionary computing techniques for constrained continuous optimisation

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaining close to optimal solutions. Using a multi-objective approach, we evolve constrained continuous problems having a set of linear and/or quadratic constraints where the different evolutionary approaches show a significant difference in performance. Afterwards, we discuss the features of the constraints that exhibit a difference in performance of the different evolutionary approaches under consideration.Shayan Poursoltan and Frank Neuman

To de or not to de? multi-objective differential evolution revisited from a component-wise perspective

Differential evolution (DE) research for multi-objective optimization can be divided into proposals that either consider DE as a stand-alone algorithm, or see DE as an algorithmic component that can be coupled with other algorithm components from the general evolutionary multiobjective optimization (EMO) literature. Contributions of the latter type have shown that DE components can greatly improve the performance of existing algorithms such as NSGA-II, SPEA2, and IBEA. However, several experimental factors have been left aside from that type of algorithm design, compromising its generality. In this work, we revisit the research on the effectiveness of DE for multi-objective optimization, improving it in several ways. In particular, we conduct an iterative analysis on the algorithmic design space, considering DE and environmental selection components as factors. Results show a great level of interaction between algorithm components, indicating that their effectiveness depends on how they are combined. Some designs present state-of-theart performance, confirming the effectiveness of DE for multi-objective optimization.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

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