Metco: A Parallel Plugin-Based Framework for Multi-Objective Optimization

León, Coromoto; Miranda, Gara; Segura, Carlos

doi:10.1142/s0218213009000275

Cited by 44 publications

(22 citation statements)

References 12 publications

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“…The approach de/rand/1/bin, as well as the initialisation strategies considered, were implemented by using the Meta-heuristic-based Extensible Tool for Cooperative Optimisation (metco) [7]. Tests were run on Teide High Performance Computing facilities, which are composed of 1100 Fujitsu® computer servers, with a total of 17800 computing cores and 36 tb of memory.…”

Section: Experimental Methodsmentioning

confidence: 99%

On the comparison of initialisation strategies in differential evolution for large scale optimisation

et al. 2017

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Differential Evolution (de) has shown to be a promising global optimisation solver for continuous problems, even for those with a large dimensionality. Different previous works have studied the effects that a population initialisation strategy has on the performance of de when solving large scale continuous problems, and several contradictions have appeared with respect to the benefits that a particular initialisation scheme might provide. Some works have claimed that by applying a particular approach to a given problem, the performance of de is going to be better than using others. In other cases however, researchers have stated that the overall performance of de is not going to be affected by the use of a particular initialisation method. In this work, we study a wide range of well-known initialisation techniques for de. Taking into account the best and worst results, statistically significant differences among considered initialisation strategies appeared. Thus, with the aim of increasing the probability of appearance of high-quality results and/or reducing the probability of appearance of low-quality ones, a suitable initialisation strategy, which depends on the large scale problem being solved, should be selected.

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Section: Experimental Methodsmentioning

confidence: 99%

On the comparison of initialisation strategies in differential evolution for large scale optimisation

et al. 2017

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“…All the approaches just described were implemented through the Meta-heuristic-based Extensible Tool for Cooperative Optimisation ( ) [6].…”

Section: Experimental Evaluationmentioning

confidence: 99%

A novel similarity-based mutant vector generation strategy for differential evolution

Segredo

Lalla-Ruiz

Hart

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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e mutant vector generation strategy is an essential component of Di erential Evolution ( ), introduced to promote diversity, resulting in exploration of novel areas of the search space. However, it is also responsible for promoting intensi cation, to improve those solutions located in promising regions. In this paper we introduce a novel similarity-based mutant vector generation strategy for , with the goal of inducing a suitable balance between exploration and exploitation, adapting its behaviour depending on the current state of the search. In order to achieve this balance, the strategy considers similarities among individuals in terms of their Euclidean distance in the decision space. A variant of incorporating the novel mutant vector generation strategy is compared to well-known explorative and exploitative adaptivevariants. An experimental evaluation performed on a well-known suite of large-scale continuous problems shows that the new algorithm that makes use of the similarity-based approach provides be er performance in comparison to the explorative and exploitative variants for a wide range of the problems tested, demonstrating the ability of the new component to properly balance exploration and exploitation.

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“…The Meta-heuristic-based Extensible Tool for Cooperative Optimization (METCO) [41] was used to implement our proposals. Experiments were run on bi-processor machines with 32 Gb RAM.…”

Section: Experimental Validationmentioning

confidence: 99%

The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles

Segura

Segredo

Miranda

2017

2017 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-In recent years, several memetic algorithms with explicit mechanisms to delay convergence have shown great promise when solving 9x9 Sudoku puzzles. This paper analyzes and extends state-of-the-art schemes for dealing with Sudoku puzzles of larger dimensionality. Two interesting aspects are analyzed: the importance of the encoding and its relation with the way of managing the diversity. Specifically, three different ways of encoding the individuals and six different methods, including four that control the diversity in a special way, are studied. Computational results are shown with twenty 16x16 Sudoku puzzles. Contrary to the low-dimensional case, important differences appear among the several ways of controlling diversity. Specifically, a method that incorporates multi-objective concepts in the replacement phase to deal with the diversity, resulted in the most promising method. Results show that both the encoding and the way of managing diversity are crucial to attain high success probabilities in large Sudoku puzzles. They also show that, while the analyzed encodings induce different search space sizes, this feature is not enough to justify the differences in the performance attained by them.

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Metco: A Parallel Plugin-Based Framework for Multi-Objective Optimization

Cited by 44 publications

References 12 publications

On the comparison of initialisation strategies in differential evolution for large scale optimisation

On the comparison of initialisation strategies in differential evolution for large scale optimisation

A novel similarity-based mutant vector generation strategy for differential evolution

The importance of the individual encoding in memetic algorithms with diversity control applied to large Sudoku puzzles

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