NDLI: Adaptive direction information in differential evolution for numerical optimization

Content Provider	Springer Nature Link
Author	Cai, Yiqiao Wang, Jiahai Chen, Yonghong Wang, Tian Tian, Hui Luo, Wei
Copyright Year	2014
Abstract	Differential evolution (DE) is a powerful evolutionary algorithm (EA) for numerical optimization. It has been successfully used in various scientific and engineering fields. In most of the DE algorithms, the neighborhood and direction information are not fully and simultaneously exploited to guide the search. Most recently, to make full use of these information, a DE framework with neighborhood and direction information (NDi-DE) was proposed. It was experimentally demonstrated that NDi-DE was effective for most of the DE algorithms. However, the performance of NDi-DE heavily depends on the selection of direction information. To alleviate this drawback and improve the performance of NDi-DE, the adaptive operator selection (AOS) mechanism is introduced into NDi-DE to adaptively select the direction information for the specific DE mutation strategy. Therefore, a new DE framework, adaptive direction information based NDi-DE (aNDi-DE), is proposed in this study. With AOS, the good balance between exploration and exploitation of aNDi-DE can be dynamically achieved. In order to evaluate the effectiveness of aNDi-DE, the proposed framework is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results show that aNDi-DE is able to adaptively select the most suitable type of direction information for the specific DE mutation strategy during the evolutionary process. The efficiency and robustness of aNDi-DE are also confirmed by comparing with NDi-DE.
Starting Page	465
Ending Page	494
Page Count	30
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	2
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-11-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Differential evolution Neighborhood information Direction information Adaptive operator selection Numerical optimization Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Cellular direction information based differential evolution for numerical optimization: an empirical study

Learning-enhanced differential evolution for numerical optimization

Enhanced differential evolution using random-based sampling and neighborhood mutation

An improved memetic algorithm using ring neighborhood topology for constrained optimization

A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization

Scalability of generalized adaptive differential evolution for large-scale continuous optimization

MODE: multiobjective differential evolution for feature selection and classifier ensemble

Differential evolution optimization with time-frame strategy adaptation

Self-adaptive, multipopulation differential evolution in dynamic environments

Adaptive direction information in differential evolution for numerical optimization

Similar Documents

Cellular direction information based differential evolution for numerical optimization: an empirical study

Learning-enhanced differential evolution for numerical optimization

Enhanced differential evolution using random-based sampling and neighborhood mutation

An improved memetic algorithm using ring neighborhood topology for constrained optimization

A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization

Scalability of generalized adaptive differential evolution for large-scale continuous optimization

MODE: multiobjective differential evolution for feature selection and classifier ensemble

Differential evolution optimization with time-frame strategy adaptation

Self-adaptive, multipopulation differential evolution in dynamic environments

Adaptive direction information in differential evolution for numerical optimization