JACIII Vol.9 No.3 pp. 282-289
doi: 10.20965/jaciii.2005.p0282


Constrained Optimization by the α Constrained Particle Swarm Optimizer

Tetsuyuki Takahama*, and Setsuko Sakai**

*Faculty of Information Sciences, Hiroshima City University, 3-4-1 Ozukahigashi, Asaminami-Ku, Hiroshima 731-3194, Japan

**Faculty of Commercial Sciences, Hiroshima Shudo University, 1-1-1 Ozukahigashi, Asaminami-Ku, Hiroshima 731-3195, Japan

October 29, 2004
December 25, 2004
May 20, 2005
constrained optimization, nonlinear optimization, particle swarm optimization, α constrained method
In this study, α constrained particle swarm optimizer αPSO, which is the combination of the α constrained method and particle swarm optimization, is proposed to solve constrained optimization problems. The α constrained methods can convert algorithms for unconstrained problems to algorithms for constrained problems using the α level comparison, which compares the search points based on the satisfaction level of constraints. In the αPSO, the agents who satisfy the constraints move to optimize the objective function and the agents who don't satisfy the constraints move to satisfy the constraints. The effectiveness of the αPSO is shown by comparing the αPSO with GENOCOP5.0, and other PSO-based methods on some nonlinear constrained problems.
Cite this article as:
T. Takahama and S. Sakai, “Constrained Optimization by the α Constrained Particle Swarm Optimizer,” J. Adv. Comput. Intell. Intell. Inform., Vol.9 No.3, pp. 282-289, 2005.
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Last updated on May. 10, 2024