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Autonomous multi-swarm optimizer for dynamic environments inspired by the partial synchronization in coupled chaotic oscillator networks

Research Project

Project/Area Number 17H06552
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeSingle-year Grants
Research Field Soft computing
Research InstitutionUtsunomiya University

Principal Investigator

Yoshikazu Yamanaka  宇都宮大学, 工学部, 助教 (00804238)

Project Period (FY) 2017-08-25 – 2019-03-31
Project Status Completed (Fiscal Year 2018)
Budget Amount *help
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Keywords群れ形成 / 最適化問題 / 群知能 / 力学系 / 粒子群最適化 / 適応制御 / カオス / 動的最適化 / 非線形力学系
Outline of Final Research Achievements

Optimizers finding multiple optimal or quasi-optimal solutions in a single run are required especially for the real-world problems in dynamic environments. In this research, we proposed a novel multi-swarm optimizer, in which multiple subswarms can dynamically be organized around the optimal solutions. In particular, we modified particle swarm optimization by implementing a simple gravitational force between particles. In the proposed method, the particles not only form multiple subswarms but also escape from the corresponding subswarm, by which the proposed method was able to find near and distant optimal (or quasi-optimal) solutions. The mechanism of these search motions was revealed by analyzing the effects of the simple gravitational force. Furthermore, based on this analysis, the exploitation and exploration capability of the proposed method were controlled. As the results, the performance of proposed method was remarkably improved.

Academic Significance and Societal Importance of the Research Achievements

予測不可能な環境下における最適化問題では,複数の最適解もしくは実用上十分に優良な解を並列に探索する必要がある.従来,最適解を探索するエージェントを複数の群れに分割する手法が提案されているが,多くの場合,その群れの数や1つの群れあたりのエージェント数をユーザが事前に定める必要があった.
本研究では,群れが自律的に創発される新たな手法を提案し,従来は不可欠であったユーザによる群れ構造の定義を不要にした.さらに,提案手法はパラメータ数と計算コストが従来法よりも小さいにも関わらず,より高い性能を実現可能であることを明らかにした.本成果は多様な実問題におけるリアルタイムな最適化の実現に寄与するものである.

Report

(3 results)
  • 2018 Annual Research Report   Final Research Report ( PDF )
  • 2017 Annual Research Report
  • Research Products

    (2 results)

All 2018

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (1 results) (of which Int'l Joint Research: 1 results)

  • [Journal Article] Tracking optima in dynamic problems by an optimizer based on piecewise-rotational chaos system2018

    • Author(s)
      Yamanaka Yoshikazu、Tsubone Tadashi
    • Journal Title

      Nonlinear Theory and Its Applications, IEICE

      Volume: 9 Issue: 4 Pages: 497-516

    • DOI

      10.1587/nolta.9.497

    • NAID

      130007491227

    • ISSN
      2185-4106
    • Related Report
      2018 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] Monte Carlo analysis of gathering and scattering behavior generated by gravitational particle swarm algorithm2018

    • Author(s)
      Yoshikazu Yamanaka and Katsutoshi Yoshida
    • Organizer
      The 50th ISCIE International Symposium on Stochastic Systems Theory and Its Applications
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research

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Published: 2017-08-25   Modified: 2020-03-30  

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