| 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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| 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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