Algorithm Structure Design for Evolutionary Multi-Objective Local Search
| Project/Area Number |
24300090
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
Ishibuchi Hisao 大阪府立大学, 工学(系)研究科(研究院), 教授 (60193356)
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| Project Period (FY) |
2012-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2014: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2013: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
Fiscal Year 2012: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
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| Keywords | アルゴリズム / 多目的最適化 / 遺伝的アルゴリズム / 進化計算 / 遺伝的局所探索 / 遺伝的局所最適化 / 最適化アルゴリズム / メタレベル最適化 / アルゴリズム構造 / アルゴリズム自動生成 |
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| Outline of Final Research Achievements |
When we search for a final single solution of a multi-objective optimization problem with conflicting objectives, we need to identify the tradeoff relation among the conflicting objectives. For this purpose, it is important to find a set of so-called Pareto optimal solutions. This is because the set of all Pareto optimal solutions, which is called the Pareto front, shows the tradeoff relation among the conflicting objectives in the objective space. An evolutionary multi-objective optimization (EMO) algorithm tries to find all Pareto optimal solutions by its single run using its multi-point search property. In this project, we have examined how to combine local search with an EMO algorithm for drastically improving its search ability. After examining various schemes for combining local search, we obtained some important guidelines for implementing high-performance evolutionary multi-objective local search.
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Report
(5 results)
Research Products
(30 results)
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[Presentation] Relation between weight vectors and solutions in MOEA/D2015
Author(s)
H. Ishibuchi, K. Doi, H. Masuda, and Y. Nojima
Organizer
2015 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making
Place of Presentation
Cape Town International Convention Center, Cape Town, South Africa
Year and Date
2015-12-08
Related Report
Int'l Joint Research
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[Presentation] Evolutionary many-objective optimization2015
Author(s)
H. Ishibuchi
Organizer
4th International Conference on Frontiers in Intelligent Computing: Theory and Applications
Place of Presentation
Peerless Sarovar Portico, Durgapur, India
Year and Date
2015-11-16
Related Report
Int'l Joint Research / Invited
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