Analysis of the Influence of Bound Constraint Handling Strategies on the Search Direction in Differential Evolution Algorithms

Madalina-Andreea, Mitran

doi:10.1109/synasc54541.2021.00055

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Relation to theoretical results. The experimental results illustrated in Figure 2 lead to remarks that are in line with the results derived in Section 4.3: (i) sat is more likely to lead to the highest value of cosine similarity (provable in the case where only one component is corrected -this could be related to small values of F and C r ); (ii) in the case when F ď 0.5 it is more likely that tor induces a higher disruption than mir the same quadrant); (iii) the cosine similarity between the initial search direction and that corresponding to sat is always positive and higher than that corresponding to HVB (Mitran, 2021).…”

Section: Analysis Of Results On Distributions Of Cosine Similaritymentioning

confidence: 99%

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond

Kononova

Vermetten

Caraffini

et al. 2024

Evolutionary Computation

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We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple bound constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours in terms of performance, disruptiveness and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants, on a special test function and the BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem's dimensionality. Differential Evolution is not at all special in this regard - there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the heuristic optimisation community to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we refer to as the strategy of dealing with infeasible solutions. This component needs to be consistently: (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on an algorithm's performance in a wider sense (i.e. convergence time, robustness, etc.) and (c) included in the (automatic) design of algorithms. All of these should be done even for problems with bound constraints.

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Section: Analysis Of Results On Distributions Of Cosine Similaritymentioning

confidence: 99%

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond

Kononova

Vermetten

Caraffini

et al. 2024

Evolutionary Computation

View full text Add to dashboard Cite

show abstract

Analysis of the Influence of Bound Constraint Handling Strategies on the Search Direction in Differential Evolution Algorithms

Cited by 1 publication

References 12 publications

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond

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