Abstract
Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.
| Original language | English |
|---|---|
| Article number | 6148273 |
| Pages (from-to) | 801-816 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Evolutionary Computation |
| Volume | 16 |
| Issue number | 6 |
| Early online date | 7 Feb 2012 |
| DOIs | |
| Publication status | Published - 10 Dec 2012 |
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Keywords
- bed capacity planning
- multiobjective resource allocation problem (MORAP)
- particle swarm optimization (PSO)
- resource allocation problem (RAP)
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An efficient resource allocation scheme using particle swarm optimization. / Gong, Yue Jiao; Zhang, Jun; Chung, Henry Shu Hung; Chen, Wei Neng; Zhan, Zhi Hui; Li, Yun; Shi, Yu Hui.
In: IEEE Transactions on Evolutionary Computation, Vol. 16, No. 6, 6148273, 10.12.2012, p. 801-816.Research output: Contribution to journal › Article
TY - JOUR
T1 - An efficient resource allocation scheme using particle swarm optimization
AU - Gong, Yue Jiao
AU - Zhang, Jun
AU - Chung, Henry Shu Hung
AU - Chen, Wei Neng
AU - Zhan, Zhi Hui
AU - Li, Yun
AU - Shi, Yu Hui
PY - 2012/12/10
Y1 - 2012/12/10
N2 - Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.
AB - Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.
KW - bed capacity planning
KW - multiobjective resource allocation problem (MORAP)
KW - particle swarm optimization (PSO)
KW - resource allocation problem (RAP)
UR - http://www.scopus.com/inward/record.url?scp=84870558007&partnerID=8YFLogxK
U2 - 10.1109/TEVC.2012.2185052
DO - 10.1109/TEVC.2012.2185052
M3 - Article
AN - SCOPUS:84870558007
VL - 16
SP - 801
EP - 816
JO - IEEE Transactions on Evolutionary Computation
JF - IEEE Transactions on Evolutionary Computation
SN - 1089-778X
IS - 6
M1 - 6148273
ER -