Distance based parameter adaptation for differential evolution

Viktorin, Adam; Šenkeřík, Roman; Pluháček, Michal; Kadavý, Tomáš; Zamuda, Aleš

doi:10.1109/ssci.2017.8280959

Cited by 16 publications

(10 citation statements)

References 13 publications

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“…Therefore, the exploration ability and higher population diversity are rewarded, and this should lead to avoidance of the premature convergence in higher dimensional objective spaces. This distance based approach can be easily implemented to any variant of SHADE/L-SHADE family of algorithms [74].…”

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Korytkowski

Šenkeřík

Scherer

et al. 2019

Journal of Artificial Intelligence and Soft Computing Research

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Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

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Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Korytkowski

Šenkeřík

Scherer

et al. 2019

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

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“…As a thriving application platform, HPC excels in supporting execution and it's speedup through parallellisation when running Computational Intelligence (CI) algorithms. The likes of CI algorithms supported by this action includes development of some of most efficient optimization algorithms for continuous optimization as defined with benchmark functions competition framework from Congress on Evolutionary Computation (CEC) 2017 [143,144]. Specifically useful, in [144] a Differential Evolution (DE) algorithm is enhanced with a new mechanism, the distance based parameter adaptation in the context of Success-History based DE (SHADE), the winner strategy of several previous CEC competitions.…”

Section: Hpc-enabled Modelling and Simulation For Socio-economical Anmentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

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Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

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“…Distance approach is based on the Euclidean distance between the trial and the original individual, which slightly increases the complexity of the algorithm by exchanging simple difference for Euclidean distance computation for the price of stronger exploration. In this case, scaling factor and crossover rate values connected with the individual that moved the furthest will have the highest weight (12).…”

Section: Db_shadementioning

confidence: 99%

“…One of the most successful variants with self-adaptive mechanism is Successful-History based Adaptive Differential Evolution (SHADE) [7], which formed a basis for latest winners of the CEC competition in continuous optimization -2013 SHADE 3 rd , 2014 L-SHADE 1 st [8], 2015 SPS-L-SHADE-EIG 1 st [9], 2016 LSHADE_EpSin 1 st [10] and 2017 jSO 1 st [11]. Therefore, it was selected for a proposal of a novel Distance based parameter adaptation in [12] and Db_SHADE was developed. Distance based adaptation should promote exploration of the algorithm and avoid premature convergence in higher dimensions.…”

Section: Introductionmentioning

confidence: 99%

Clustering Analysis of the Population in Db_SHADE Algorithm

Viktorin¹,

Šenkeřík²,

Pluháček³

et al. 2018

mendel

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This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set in two dimensional settings (10D and 30D). The clustering analysis helps to answer the question about prolonged exploration phase of the Db_SHADE algorithm. Possible future research directions are drawn in the discussion and conclusion.

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Distance based parameter adaptation for differential evolution

Cited by 16 publications

References 13 publications

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Clustering Analysis of the Population in Db_SHADE Algorithm

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