Abstract
A new strategy of Adaptive Plan System with Genetic Algorithm (APGA) is proposed to reduce a large amount of calculation cost and to improve stability in convergence to an optimal solution for multi-peak optimization problems with multi-dimensions. This is an approach that combines the global search ability of Genetic Algorithm (GA) and the local search ability of Adaptive Plan (AP). The APGA differs from GAs in handling design variable vectors (DVs). GAs generally encode DVs into genes and handle them through GA operators. However, the APGA encodes control variable vectors (CVs) of AP, which searches for local optimum, into its genes. CVs determine the global behavior of AP, and DVs are handled by AP in the optimization process of APGA. In this paper, we introduce a new approach for Adaptive Plan System of swarm intelligent using Particle Swarm Optimization (PSO) with Genetic Algorithm (PSO-APGA) to solve a huge scale optimization problem, and to improve the convergence towards the optimal solution. The PSO-APGA is applied to several benchmark functions with multi-dimensions to evaluate its performance.We confirmed satisfactory performance through various benchmark tests.
| Original language | English |
|---|---|
| Title of host publication | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Pages | 171-176 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul Duration: 2012 Oct 14 → 2012 Oct 17 |
Other
| Other | 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 |
|---|---|
| City | Seoul |
| Period | 12/10/14 → 12/10/17 |
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Keywords
- Adaptive System
- Genetic Algorithm
- Multi-peak problems
- Particle Swarm Optimization
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction
Cite this
Adaptive system of swarm intelligent with Genetic Algorithm for global optimization. / Pham, Hieu; Hasegawa, Hiroshi.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. p. 171-176 6377695.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive system of swarm intelligent with Genetic Algorithm for global optimization
AU - Pham, Hieu
AU - Hasegawa, Hiroshi
PY - 2012
Y1 - 2012
N2 - A new strategy of Adaptive Plan System with Genetic Algorithm (APGA) is proposed to reduce a large amount of calculation cost and to improve stability in convergence to an optimal solution for multi-peak optimization problems with multi-dimensions. This is an approach that combines the global search ability of Genetic Algorithm (GA) and the local search ability of Adaptive Plan (AP). The APGA differs from GAs in handling design variable vectors (DVs). GAs generally encode DVs into genes and handle them through GA operators. However, the APGA encodes control variable vectors (CVs) of AP, which searches for local optimum, into its genes. CVs determine the global behavior of AP, and DVs are handled by AP in the optimization process of APGA. In this paper, we introduce a new approach for Adaptive Plan System of swarm intelligent using Particle Swarm Optimization (PSO) with Genetic Algorithm (PSO-APGA) to solve a huge scale optimization problem, and to improve the convergence towards the optimal solution. The PSO-APGA is applied to several benchmark functions with multi-dimensions to evaluate its performance.We confirmed satisfactory performance through various benchmark tests.
AB - A new strategy of Adaptive Plan System with Genetic Algorithm (APGA) is proposed to reduce a large amount of calculation cost and to improve stability in convergence to an optimal solution for multi-peak optimization problems with multi-dimensions. This is an approach that combines the global search ability of Genetic Algorithm (GA) and the local search ability of Adaptive Plan (AP). The APGA differs from GAs in handling design variable vectors (DVs). GAs generally encode DVs into genes and handle them through GA operators. However, the APGA encodes control variable vectors (CVs) of AP, which searches for local optimum, into its genes. CVs determine the global behavior of AP, and DVs are handled by AP in the optimization process of APGA. In this paper, we introduce a new approach for Adaptive Plan System of swarm intelligent using Particle Swarm Optimization (PSO) with Genetic Algorithm (PSO-APGA) to solve a huge scale optimization problem, and to improve the convergence towards the optimal solution. The PSO-APGA is applied to several benchmark functions with multi-dimensions to evaluate its performance.We confirmed satisfactory performance through various benchmark tests.
KW - Adaptive System
KW - Genetic Algorithm
KW - Multi-peak problems
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=84872383796&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872383796&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2012.6377695
DO - 10.1109/ICSMC.2012.6377695
M3 - Conference contribution
AN - SCOPUS:84872383796
SN - 9781467317146
SP - 171
EP - 176
BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ER -