What are dynamic optimization problems?

Haobo Fu; Peter R. Lewis; Bernhard Sendhoff; Ke Tang; Xin Yao

doi:10.1109/CEC.2014.6900316

What are dynamic optimization problems?

Haobo Fu^*
, Peter R. Lewis
, Bernhard Sendhoff
, Ke Tang
, Xin Yao

^*Corresponding author for this work

Computer Science Research Group

Honda Research Institute Europe
University of Science and Technology of China (Hefei)
University College Birmingham

Research output: Chapter in Book/Published conference output › Conference publication

25 Citations (Scopus)

869 Downloads (Pure)

Abstract

Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	IEEE
Pages	1550-1557
Number of pages	8
ISBN (Print)	978-1-4799-6626-4
DOIs	https://doi.org/10.1109/CEC.2014.6900316
Publication status	Published - 2014
Event	2014 IEEE Congress on Evolutionary Computation - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Congress

Congress	2014 IEEE Congress on Evolutionary Computation
Abbreviated title	CEC 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

Bibliographical note

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Access to Document

10.1109/CEC.2014.6900316

Dynamic optimization problems
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 209 KB

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6900316

Cite this

@inproceedings{a06c3112d1bf4479b7d59f44b7398aff,

title = "What are dynamic optimization problems?",

abstract = "Dynamic Optimization Problems (DOPs) have been widely studied using Evolutionary Algorithms (EAs). Yet, a clear and rigorous definition of DOPs is lacking in the Evolutionary Dynamic Optimization (EDO) community. In this paper, we propose a unified definition of DOPs based on the idea of multiple-decision-making discussed in the Reinforcement Learning (RL) community. We draw a connection between EDO and RL by arguing that both of them are studying DOPs according to our definition of DOPs. We point out that existing EDO or RL research has been mainly focused on some types of DOPs. A conceptualized benchmark problem, which is aimed at the systematic study of various DOPs, is then developed. Some interesting experimental studies on the benchmark reveal that EDO and RL methods are specialized in certain types of DOPs and more importantly new algorithms for DOPs can be developed by combining the strength of both EDO and RL methods.",

author = "Haobo Fu and Lewis, \{Peter R.\} and Bernhard Sendhoff and Ke Tang and Xin Yao",

note = "{\textcopyright} 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

doi = "10.1109/CEC.2014.6900316",

language = "English",

isbn = "978-1-4799-6626-4",

pages = "1550--1557",

booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",