NDLI: Convergence of stochastic search algorithms to finite size pareto set approximations

Content Provider	Springer Nature Link
Author	Dellnitz, Michael Laumanns, Marco Coello Coello, Carlos A. Schütze, Oliver Talbi, El Ghazali
Copyright Year	2008
Abstract	In this work we investigate the convergence of stochastic search algorithms toward the Pareto set of continuous multi-objective optimization problems. The focus is on obtaining a finite approximation that should capture the entire solution set in a suitable sense, which will be defined using the concept of ε-dominance. Under mild assumptions about the process to generate new candidate solutions, the limit approximation set will be determined entirely by the archiving strategy. We propose and analyse two different archiving strategies which lead to a different limit behavior of the algorithms, yielding bounds on the obtained approximation quality as well as on the cardinality of the resulting Pareto set approximation.
Ending Page	577
Page Count	19
Starting Page	559
File Format	PDF
ISSN	09255001
e-ISSN	15732916
Journal	Journal of Global Optimization
Issue Number	4
Volume Number	41
Language	English
Publisher	Springer US
Publisher Date	2008-02-05
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Models, numerical methods Operations Research/Decision Theory Management decision making, including multiple objectives Computer Science Multi-objective optimization Stochastic search algorithms Optimization Convergence ε-dominance Real Functions
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Applied Mathematics Management Science and Operations Research Business, Management and Accounting Computer Science Applications

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

Convergence of Stochastic Search Algorithms to Finite Size Pareto Set Approximations

Convergence of Stochastic Search Algorithms to Finite Size Pareto Set Approximations

Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

Optimization over the Pareto outcome set associated with a convex bi-objective optimization problem: theoretical results, deterministic algorithm and application to the stochastic case

A hybrid method for solving multi-objective global optimization problems

Multiplier Rules and Saddle-Point Theorems for Helbig’s Approximate Solutions in Convex Pareto Problems

Computing the Set of Approximate Solutions of an MOP with Stochastic Search Algorithms

Explicitly quasiconvex set-valued optimization

ParEGO extensions for multi-objective optimization of expensive evaluation functions

Convergence of stochastic search algorithms to finite size pareto set approximations

Similar Documents

Convergence of Stochastic Search Algorithms to Finite Size Pareto Set Approximations

Convergence of Stochastic Search Algorithms to Finite Size Pareto Set Approximations

Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

Optimization over the Pareto outcome set associated with a convex bi-objective optimization problem: theoretical results, deterministic algorithm and application to the stochastic case

A hybrid method for solving multi-objective global optimization problems

Multiplier Rules and Saddle-Point Theorems for Helbig’s Approximate Solutions in Convex Pareto Problems

Computing the Set of Approximate Solutions of an MOP with Stochastic Search Algorithms

Explicitly quasiconvex set-valued optimization

ParEGO extensions for multi-objective optimization of expensive evaluation functions

Convergence of stochastic search algorithms to finite size pareto set approximations