Development, Evaluation and Application of Multi-objective Optimization Methods by Genetic Algorithms
| Project/Area Number |
12680402
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
NISHIKAWA Ikuko Ritsumeikan Univ., Fac. Science and Engineering, Associate Professor, 理工学部, 助教授 (90212117)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2001: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2000: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Genetic Algorithm / Multi-objective Optimization / Parato Set / Coding / Parallel Selection / Correlation Coefficient / Evaluation / Knapsack Problem / 多目的最適化 / デコーディング / 並列選択 / パレート解 / ナップザック問題 / フローショップ問題 |
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| Research Abstract |
1. Multiple Coding Genetic Algorithm (MCGA) is proposed as a multi-objective GA method, in which each individual specializes on one of many objectives and has a different decoding according each objective. The comparison with a conventional parallel selection method by computer experiment shows the complementary performance in searching, therefore a hybrid method of the both is also proposed.
2. The proposed method is applied 2-objective knapsack problems and 2-objective flowshop scheduling problem, and its performance is compared with the ordinal parallel selection.
(1) In the application to knapsack problems, the data sets with different correlation coefficients are generated in a systematic way to control the distribution of a Pareto set.
(2) In the application to flowshop scheduling, heuristic decoding methods are proposed for each objective.
3. Quantitative evaluations of the obtained solutions are given by relative accuracy, cover ratio, diversity and the number of acquired solutions.
4. Simulation result shows that the proposed method is effective to obtain diverse solutions especially in the problem with a large Pareto set, which results either from a statistical character of a given problem, from a largeness of a problem scale, or from a high dimensionality of a solution space.
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Report
(3 results)
Research Products
(20 results)
