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Using binary particle swarm optimization to search for maximal successful coalition

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Abstract

Coalitional Resource Games (CRGs) are a natural and formal framework in which agents wish to form coalitions to pool their scarce resources in order to achieve a set of goals that satisfy all members of a coalition. Thus far, many computational questions surrounding CRGs have been studied, but to our knowledge, a number of natural decision problems in CRGs have not been solved. Therefore, in this paper we investigate the possibility of using binary particle swarm optimization (BPSO) as a stochastic search process to search for Maximal Successful Coalition (MAXSC) in CRGs, which is a DP-complete problem. For this purpose, we develop a one-dimensional binary encoding scheme, propose strategies for encoding repair to ensure that each encoding in every iteration process is approximately valid and logicallsy consistent, and discuss some key properties of repair strategies. To evaluate the effectiveness of our algorithms, we compare them with the only other algorithm available in the literature for identifying MAXSC (due to Shrot, Aumann, and Kraus). The result shows that our algorithms are significantly faster especially for large-scale datasets.

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Notes

  1. Note that the complexity of encoding repair for two-dimensional binary encoding is 𝓞(n 4) (see Zhang et al. [31]).

  2. Note that the complexity of SAK is 𝓞(n 2), which is of exponential complexity (see Shrot et al. [24]).

  3. Here, 6 has been tested to be the optimal solution in the same CRGs instance by SAK which is an exhaustive algorithm.

  4. This is because coalitions which are bigger than C have been checked to be unsuccessful by SAK.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61174170, 61100127, 61371155 and the Science and Technology Key Projects of Anhui Province under Grant 1301b042023. In addition, the authors would like to thank the Editors and the anonymous referees for their comments and suggestions.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, China

    Guofu Zhang, Renzhi Yang, Zhaopin Su, Feng Yue, Yuqi Fan, Meibin Qi & Jianguo Jiang

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  1. Guofu Zhang
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  2. Renzhi Yang
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Correspondence to Guofu Zhang.

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Zhang, G., Yang, R., Su, Z. et al. Using binary particle swarm optimization to search for maximal successful coalition. Appl Intell 42, 195–209 (2015). https://doi.org/10.1007/s10489-014-0589-y

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