2013 Fiscal Year Final Research Report
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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| Keywords | 差分進化 / 組合せ最適化 / 進化計算 |
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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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