dc.contributor.author |
Helbig, M
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|
dc.contributor.author |
Engelbrecht, AP
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dc.date.accessioned |
2012-11-19T14:50:13Z |
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dc.date.available |
2012-11-19T14:50:13Z |
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dc.date.issued |
2012 |
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dc.identifier.citation |
Helbig, M and Engelbrecht, AP. Dynamic multi-objective optimization using PSO. In: Alba, E, Nakib, A, and Siarry, P (eds), Metaheuristics for Dynamic Optimization, (Studies in Computational Intelligence, Volume 433), pp. 147-188, DOI 10.1007/978-3-642-30665-5_8. Springer, Berlin, Germany. |
en_US |
dc.identifier.isbn |
978-3-642-30664-8 |
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dc.identifier.isbn |
978-3-642-30665-5 |
|
dc.identifier.uri |
https://www.springer.com/pay+per+view?SGWID=0-1740713-3131-0-0
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|
dc.identifier.uri |
http://hdl.handle.net/10204/6330
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|
dc.description |
Copyright: 2013 SpringerVerlag. This is the post-print version of the work. The definitive version is published in Alba, E, Nakib, A, and Siarry, P (eds), Metaheuristics for Dynamic Optimization, (Studies in Computational Intelligence, Volume 433), pp. 147-188, DOI 10.1007/978-3-642-30665-5_8 |
en_US |
dc.description.abstract |
Dynamic multi-objective optimisation problems (DMOOPs) occur in many situations in the real world. These optimisation problems do not have a single goal to solve, but many goals that are in conflict with one another - improvement in one goal leads to deterioration of another. Therefore, when solving DMOOPs, an algorithm attempts to find the set of optimal solutions, referred to as the Paretooptimal front (POF). Each DMOOP also has a number of boundary constraints that limits the search space. When the particles of a particle swarm optimisation (PSO) algorithm moves outside the search space, an approach should be followed to manage violation of the boundary constraints. This chapter investigates the effect of various approaches to manage boundary constraint violations on the performance of the Dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO) algorithm when solving DMOOPs. Furthermore, the performance of DVEPSO is compared against the performance of three other state-of-the-art dynamic multi-objective optimisation (DMOO) algorithms. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Springer |
en_US |
dc.relation.ispartofseries |
Workflow;9690 |
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dc.subject |
Dynamic multi-objective optimisation |
en_US |
dc.subject |
Particle swarm optimisation |
en_US |
dc.title |
Dynamic multi-objective optimization using PSO |
en_US |
dc.type |
Book Chapter |
en_US |
dc.identifier.apacitation |
Helbig, M., & Engelbrecht, A. (2012). Dynamic multi-Objective optimization using PSO., <i>Workflow;9690</i> Springer. http://hdl.handle.net/10204/6330 |
en_ZA |
dc.identifier.chicagocitation |
Helbig, M, and AP Engelbrecht. "Dynamic multi-objective optimization using PSO" In <i>WORKFLOW;9690</i>, n.p.: Springer. 2012. http://hdl.handle.net/10204/6330. |
en_ZA |
dc.identifier.vancouvercitation |
Helbig M, Engelbrecht A. Dynamic multi-objective optimization using PSO.. Workflow;9690. [place unknown]: Springer; 2012. [cited yyyy month dd]. http://hdl.handle.net/10204/6330. |
en_ZA |
dc.identifier.ris |
TY - Book Chapter
AU - Helbig, M
AU - Engelbrecht, AP
AB - Dynamic multi-objective optimisation problems (DMOOPs) occur in many situations in the real world. These optimisation problems do not have a single goal to solve, but many goals that are in conflict with one another - improvement in one goal leads to deterioration of another. Therefore, when solving DMOOPs, an algorithm attempts to find the set of optimal solutions, referred to as the Paretooptimal front (POF). Each DMOOP also has a number of boundary constraints that limits the search space. When the particles of a particle swarm optimisation (PSO) algorithm moves outside the search space, an approach should be followed to manage violation of the boundary constraints. This chapter investigates the effect of various approaches to manage boundary constraint violations on the performance of the Dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO) algorithm when solving DMOOPs. Furthermore, the performance of DVEPSO is compared against the performance of three other state-of-the-art dynamic multi-objective optimisation (DMOO) algorithms.
DA - 2012
DB - ResearchSpace
DP - CSIR
KW - Dynamic multi-objective optimisation
KW - Particle swarm optimisation
LK - https://researchspace.csir.co.za
PY - 2012
SM - 978-3-642-30664-8
SM - 978-3-642-30665-5
T1 - Dynamic multi-objective optimization using PSO
TI - Dynamic multi-objective optimization using PSO
UR - http://hdl.handle.net/10204/6330
ER -
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en_ZA |