NDLI: Large scale optimization by differential evolution with landscape modality detection and a diversity archive

Content Provider	IEEE Xplore Digital Library
Author	Takahama, T. Sakai, S.
Copyright Year	2012
Description	Author affiliation: Department of Intelligent Systems, Hiroshima City University, Asaminami-ku, 731-3194 Japan (Takahama, T.) \|\| Faculty of Commercial Sciences, Hiroshima Shudo University, Asaminami-ku, 731-3195 Japan (Sakai, S.)
Abstract	In this study, the performance of Differential Evolution with landscape modality detection and a diversity archive (LMDEa) is reported on the set of benchmark functions provided for the CEC2012 Special Session on Large Scale Global Optimization. In Differential Evolution (DE), large population size, which is much larger than the number of decision variables in problem to be solved, is adopted in order to keep the diversity of search. However, it is difficult to adopt such large size to solve large scaled optimization problems because the population size will become too large and the search efficiency will degrade. In this study, we propose to solve large scale optimization problems using small population size and a large archive for diversity. Also, we propose simple control of scaling factor by observing landscape modality of search points in order to keep diversity. The landscape of a problem to be optimized is often unknown and the landscape is changing dynamically while the search process proceeds. In LMDEa, some points on a line connecting the centroid of search points and a search point are sampled. When the objective values of the sampled points are changed decreasingly and then increasingly, it is thought that one valley exists. If there exists only one valley, the landscape is unimodal and small scaling factor is adopted. Otherwise, large scaling factor is adopted. Also, the sampled points realize global search in the region spanned by all search points and realize local search near the best search point. The effect of the proposed method is shown by solving the benchmark functions.
Starting Page	1
Ending Page	8
File Size	941570
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6252911
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Optimization Search problems Benchmark testing Convergence Upper bound Iron parameter control differential evolution large scale optimization landscape modality
Content Type	Text
Resource Type	Article

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

Differential evolution with dynamic strategy and parameter selection by detecting landscape modality

Rank-based differential evolution with multiple mutation strategies for large scale global optimization

A hybrid harmony search algorithm combined with differential evolution for global optimization problems

Sequential DE enhanced by neighborhood search for Large Scale Global Optimization

Fruitless search in differential evolution

Efficient nonlinear optimization by differential evolution with a rotation-invariant local sampling operation

Ensemble Differential Evolution with dynamic subpopulations and adaptive clearing for solving dynamic optimization problems

Self-adaptive differential evolution algorithm with a small and varying population size

A new differential evolution algorithm for complex constrained optimization problems

Large scale optimization by differential evolution with landscape modality detection and a diversity archive

Similar Documents

Differential evolution with dynamic strategy and parameter selection by detecting landscape modality

Rank-based differential evolution with multiple mutation strategies for large scale global optimization

A hybrid harmony search algorithm combined with differential evolution for global optimization problems

Sequential DE enhanced by neighborhood search for Large Scale Global Optimization

Fruitless search in differential evolution

Efficient nonlinear optimization by differential evolution with a rotation-invariant local sampling operation

Ensemble Differential Evolution with dynamic subpopulations and adaptive clearing for solving dynamic optimization problems

Self-adaptive differential evolution algorithm with a small and varying population size

A new differential evolution algorithm for complex constrained optimization problems

Large scale optimization by differential evolution with landscape modality detection and a diversity archive