Formulation and Solution for Evaluation-times Constrained Optimization

• Project/Area Number: 16K18121
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Control engineering/System engineering
• Research Institution: Tokyo Metropolitan University
• Principal Investigator: Kenichi Tamura 首都大学東京, システムデザイン研究科, 助教 (40534912)
• Project Period (FY): 2016-04-01 – 2020-03-31
• Project Status: Completed (Fiscal Year 2019)
• Budget Amount: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000); Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000); Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000); Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
• Keywords: 最適化 / ブラックボックス問題 / black-box optimization / direct search / evaluation times / optimization / metaheurisics / descent direction / convergence / アルゴリズム / 応用数学 / システム工学

Outline of Final Research Achievements

This research project was concerned with the problem of finding the best possible solution under the constraint of the number of evaluations of the objective function specified in advance. The aim was to present the value and novelty of this problem widely and to construct solutions to solve this problem. The results obtained during this research period can be classified as follows. (1) The engineering value of this problem and the novelty of the required solution specifications were discussed and presented. (2) A direct search theory and a non-convex analysis method that can contribute to the construction of solutions to this problem were constructed. (3) Solutions to this problem based on Bayesian optimization algorithm and evolutionary algorithm were proposed.

Academic Significance and Societal Importance of the Research Achievements

本研究成果の意義は，様々な技術の進歩に伴い，大規模・複雑化したシステムに対する最適化の応用が広がる中で，従来の最適化問題とは本質的に異なる実情に即した新しい実際的問題として「評価回数制約付き最適化問題」を提起し，新しいアプローチでその具体的な解法を構築したことである。

Research Products

• [Int'l Joint Research] The University of Queensland(オーストラリア)
• [Int'l Joint Research] The University of Queensland(Australia)
• [Journal Article] Quantitative measure of nonconvexity for black-box continuous functions (2019)
  • Author(s): Kenichi Tamura, Marcus Gallagher
  • Journal Title: Information Sciences Volume: 476 Pages: 64-82
  • DOI: 10.1016/j.ins.2018.10.009
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] The Spiral Optimization Algorithm: Convergence Conditions and Settings (2017)
  • Author(s): Kenichi Tamura, Keiichiro Yasuda
  • Journal Title: IEEE Transactions on Systems, Man, and Cybernetics: Systems Volume: PP Issue: 1 Pages: 1-16
  • DOI: 10.1109/tsmc.2017.2695577
  • Peer Reviewed
• [Journal Article] Spiral Optimization Algorithm Using Periodic Descent Directions (2016)
  • Author(s): K. Tamura and K. Yasuda
  • Journal Title: SICE JCMSI Volume: 9 Issue: 3 Pages: 134-143
  • DOI: 10.9746/jcmsi.9.134
  • Peer Reviewed / Open Access
• [Presentation] Adaptive Framework for Evolutionary Algorithms to Evaluation-iteration Constrained Optimization Problems (2019)
  • Author(s): Kenichi Tamura
  • Organizer: IEEE 17th International Symposium on Intelligent Systems and Informatics
  • Int'l Joint Research
• [Presentation] The Evaluation-times Constrained Optimization (ECO) Problem and Its General Solver Model (2018)
  • Author(s): Kenichi Tamura
  • Organizer: 7th International Conference on Metaheuristics and Nature Inspired Computing
  • Int'l Joint Research
• [Presentation] A Heuristic Strategy of a Bayesian Optimization Algorithm for Evaluation-times Constrained Optimization (ECO) Problems (2018)
  • Author(s): Kenichi Tamura
  • Organizer: 2018 Tenth International Conference on Advanced Computational Intelligence
  • Int'l Joint Research