Searching for agent coalition using particle swarm optimization and death penalty function

  • Authors:

  • Sheng-Fu Zheng;Shan-Li Hu;Xian-Wei Lai;Chao-Feng Lin;She-Xiong Su

  • Affiliations:

  • Department of Computer Science and Technology, Fuzhou University, Fuzhou, China;Department of Computer Science and Technology, Fuzhou University, Fuzhou, China and Key Laboratory for Computer Science, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Technology, Fujian Agriculture and Forestry University, Fuzhou, China;Department of Computer Science and Technology, Fuzhou University, Fuzhou, China;Department of Computer Science and Technology, Fuzhou University, Fuzhou, China

  • Venue:
  • ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
  • Year:
  • 2007

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Abstract

The issue of coalition formation problem has been investigated from many aspects. However, all of the previous work just take the capability of agent into account, but not consider those factors, such as the time that agent takes to achieve a task, the cost of employing agent, the credit standing of agent, the risk that the task sponsor bears, and the bias of task sponsor and so on. So we originally take these factors into account. The coalition problem in this paper is a constrained problem including a great deal of equality constraints and inequality constraints. So we adopt the death penalty function to transform it to an unconstrained one. That is to say, it becomes a single objective function. Being an unconstrained optimization algorithm, the binary particle swarm optimization algorithm is adopted to address this problem. To improve the capability of global searching of our algorithm and convergent rate of the solutions, we divide the process of coalition formation into two stages to deal with respectively. Simulations show that our algorithm is effective and feasible.