Development of Many Population-based Differential Evolution for Combinatorial Optimization Problem and Its Application to Staff Rostering Problem
| Project/Area Number |
24700232
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Hiroshima City University |
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Principal Investigator |
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2013: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2012: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 差分進化 / 組合せ最適化 / 進化計算 / ソフトコンピューティング / 最適化 |
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| Research Abstract |
Differential evolution (DE), classified as a part of evolutionary algorithm, is a population-based stochastic search technique for solving optimization problems in a continuous space. In this research, we propose new DE algorithm to solve combinatorial optimization where decision variables are represented as a discrete value. Furthermore, we modify island based generation alternation model for DE and construct many population-based DE which can work in parallel computing platform. In the proposed method, several populations evolve competitively based on coevolutionary approach. Through the numerical experiments using a benchmark problem of staff rostering problem, we show that the proposed method is able to generate a useful roster in short period of time.
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Report
(3 results)
Research Products
(20 results)
