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International Conference on Swarm Intelligence

ICSI 2017: Advances in Swarm Intelligence pp 69–76Cite as

Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

Abstract

Multi-objective portfolio optimization (PO) problem is always converted into a single objective problem by using the weighted method, which is sensitive to the pareto optimal front and requires that decision makers must have previous experience about the preference for weights. Based on multi-objective comprehensive learning bacterial foraging optimization (MOCLBFO), this paper proposes an algorithm which is specially designed for multi-objective PO problem. The corresponding coding strategy which considers each particle as a feasible solution is also given. In order to test the validity of the algorithm, multi-objective comprehensive learning particle swarm optimization (MOCLPSO) is chosen as the competing algorithm. Comparative experimental tests on ten assets PO problem demonstrate that MOCLBFO is able to find a more well-distributed Pareto set.

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Acknowledgment

This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71271140, 61603310, 71471158, 71001072, 61472257), Natural Science Foundation of Guangdong Province (2016A030310074) and Shenzhen Science and Technology Plan (CXZZ20140418182638764), the Fundamental Research Funds for the Central Universities Nos. XDJK2014C082, XDJK2013B029, SWU114091.

Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu, Wenjie Yi, Jia Liu & Hong Wang

  2. School of Computing, Information, and Decision Systems Engineering, Arizona State University, Tempe, USA

    Ben Niu

  3. Department of Business Management, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Lijing Tan

  4. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Ya Li

  5. Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Hong Wang

Authors
  1. Ben Niu
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  2. Wenjie Yi
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  3. Lijing Tan
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  4. Jia Liu
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  5. Ya Li
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  6. Hong Wang
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Corresponding authors

Correspondence to Ben Niu , Lijing Tan or Hong Wang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Niu, B., Yi, W., Tan, L., Liu, J., Li, Y., Wang, H. (2017). Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Problem. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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