TY - GEN

T1 - Combining particle swarm with ordinal optimization for stochastic simulation optimization problems

AU - Horng, Shih Cheng

AU - Yang, Feng-Yi

PY - 2011/5/15

Y1 - 2011/5/15

N2 - In this paper, we combine the particle swarm (PS) with ordinal optimization (OO), abbreviated as CPSOO, to solve for a good enough solution of the stochastic simulation optimization problem (SSOP) with huge search space. First, a rough model using stochastic simulation with a small amount of test samples will be used as a fitness function evaluation in particle swarm optimization (PSO) algorithm to select N roughly good solutions from search space. Next, starting from the selected N roughly good solutions we proceed with goal softening procedure to search for a good enough solution. Finally, the proposed CPSOO algorithm is applied to a centralized broadband wireless network with k-limited service discipline, which is formulated as a SSOP that consists of a huge discrete search space comprised by the vector of k-limited service discipline. The vector of good enough k-limited service discipline obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency.

AB - In this paper, we combine the particle swarm (PS) with ordinal optimization (OO), abbreviated as CPSOO, to solve for a good enough solution of the stochastic simulation optimization problem (SSOP) with huge search space. First, a rough model using stochastic simulation with a small amount of test samples will be used as a fitness function evaluation in particle swarm optimization (PSO) algorithm to select N roughly good solutions from search space. Next, starting from the selected N roughly good solutions we proceed with goal softening procedure to search for a good enough solution. Finally, the proposed CPSOO algorithm is applied to a centralized broadband wireless network with k-limited service discipline, which is formulated as a SSOP that consists of a huge discrete search space comprised by the vector of k-limited service discipline. The vector of good enough k-limited service discipline obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency.

KW - centralized broadband wireless network

KW - k-limited service discipline

KW - ordinal optimization

KW - particle swarm

KW - stochastic simulation optimization

UR - http://www.scopus.com/inward/record.url?scp=80051993092&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80051993092

SN - 9788995605646

T3 - ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings

SP - 982

EP - 987

BT - ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings

T2 - 8th Asian Control Conference, ASCC 2011

Y2 - 15 May 2011 through 18 May 2011

ER -