Abstract
The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.
| Original language | English |
|---|---|
| Title of host publication | AIP Conference Proceedings |
| Pages | 7-11 |
| Number of pages | 5 |
| Volume | 1298 |
| DOIs | |
| State | Published - Dec 1 2010 |
| Event | International Conference on Modeling, Optimization, and Computing, ICMOC 2010 - Durgapur, West Bengal, India Duration: Oct 28 2010 → Oct 30 2010 |
Other
| Other | International Conference on Modeling, Optimization, and Computing, ICMOC 2010 |
|---|---|
| Country | India |
| City | Durgapur, West Bengal |
| Period | 10/28/10 → 10/30/10 |
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Keywords
- Ant colony optimization
- Constrained optimization
- Multiobjective optimizatio Game theory
- Partcle swarm optimization
- Unconstrained optimization
ASJC Scopus subject areas
- Physics and Astronomy(all)
Cite this
Particle swarm and ant colony approaches in multiobjective optimization. / Rao, Singiresu S.
AIP Conference Proceedings. Vol. 1298 2010. p. 7-11.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Particle swarm and ant colony approaches in multiobjective optimization
AU - Rao, Singiresu S
PY - 2010/12/1
Y1 - 2010/12/1
N2 - The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.
AB - The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.
KW - Ant colony optimization
KW - Constrained optimization
KW - Multiobjective optimizatio Game theory
KW - Partcle swarm optimization
KW - Unconstrained optimization
UR - http://www.scopus.com/inward/record.url?scp=79951980800&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79951980800&partnerID=8YFLogxK
U2 - 10.1063/1.3516427
DO - 10.1063/1.3516427
M3 - Conference contribution
AN - SCOPUS:79951980800
SN - 9780735408548
VL - 1298
SP - 7
EP - 11
BT - AIP Conference Proceedings
ER -