A review of opposition-based learning from 2005 to 2012

Xu, Qingzheng; Wang, Lei; Wang, Na; Hei, Xinhong; Zhao, Longfeng

doi:10.1016/j.engappai.2013.12.004

Cited by 172 publications

(76 citation statements)

References 101 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A wide range of initialisation strategies have been proposed in order to improve the results obtained by de [2,5,18]. In the current work, we compared the same set of initialisation strategies considered in [5], which are introduced herein.…”

Section: Initialisation Strategies For Differential Evolutionmentioning

confidence: 99%

“…Instead of considering randomness and/or uniformity, obl generates an initial population and calculates the opposite one with the aim of selecting the fittest individuals from both populations as the starting set. There are different variants of obl schemes [18]. In addition to the approaches considered in [5], i.e., obl and Quasi-Opposition-based Learning (qobl), we also applied Quasi-Reflection Opposition-based Learning (qrobl) herein, since a recent work [4] stated that the quasi-reflected opposition individual is more likely to be closer to the optimal solution than the opposition and quasi-opposition individuals.…”

Section: Initialisation Strategies For Differential Evolutionmentioning

confidence: 99%

“…Finally, Opposition-based Learning (obl) mechanisms as initialisation methods for de have gained a significant popularity in recent years [18]. Instead of considering randomness and/or uniformity, obl generates an initial population and calculates the opposite one with the aim of selecting the fittest individuals from both populations as the starting set.…”

Section: Initialisation Strategies For Differential Evolutionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Section: Initialisation Strategies For Differential Evolutionmentioning

confidence: 99%

Section: Initialisation Strategies For Differential Evolutionmentioning

confidence: 99%

Section: Initialisation Strategies For Differential Evolutionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

View full text Add to dashboard Cite

show abstract

“…First of all, n individuals are generated through an initialisation strategy (step 1). In this work, Opposition-based Learning ( ) [14] is applied as the initialisation mechanism. With respect to the set of large-scale problems addressed, previous work shows that the incorporation of into an explorative variant, such as /rand/1/bin, is likely to provide be er solutions than those achieved by applying other initialisation approaches [8].…”

Section: Erential Evolutionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…OBL and their extensions have been applied to improve the performance of various computational intelligence methods, such as artificial neural networks, fuzzy logic, metaheuristic algorithms, and miscellaneous applications [8].…”

Section: Opposition-based Learning (Obl)mentioning

confidence: 99%

Rotation-Based Multi-Particle Collision Algorithm with Hooke Jeeves

Torres¹,

Velho²

2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

View full text Add to dashboard Cite

Abstract. A new variant of the hybrid metaheuristic MPCA-HJ (Multi-Particle Collision Algorithm with Hooke-Jeeves method) is presented. Multi-Particle Collision Algorithm is a metaheuristic algorithm that performing a traveling on the search space. The addition of the Rotation-Based Learning mechanism to the exploration search enhances the possibility to cover a larger area in the search space. The Hooke-Jeeves direct search method exploites the best solution found by the MPCA, allowing to achieve better solutions. The performance of all implementation are evaluated over twenty-two well known benchmark functions.

show abstract

A review of opposition-based learning from 2005 to 2012

Cited by 172 publications

References 101 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

Rotation-Based Multi-Particle Collision Algorithm with Hooke Jeeves

Contact Info

Product

Resources

About