Developments of High Performance Memetic Algorithms for Combinatorial Optimization Problems
| Project/Area Number |
21500229
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Sensitivity informatics/Soft computing
|
|---|
| Research Institution | Okayama University of Science |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2009 – 2011
|
| Project Status |
Completed (Fiscal Year 2011)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2009: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
|
|---|
| Keywords | 組合せ最適化 / メタ戦略 / 進化計算 / 局所探索 / 最大クリーク問題 / ノード配置問題 / 2次割当問題 |
|---|
| Research Abstract |
In our research, we developed high-performance metaheuristic algorithms called memetic algorithms for combinatorial optimization problems. As combinatorial optimization problems, we deal with the Maximum Clique Problem(MCP), Quadratic Assignment Problem(QAP), and Node Placement Problem(NPP), which are known to be NP-hard. Particularly, for the QAP, we show an effective metaheuristic algorithm called genetic iterated local search(GILS) incorporating k-opt local search based on the idea of variable depth search. Computational results showed that the GILS with KLS obtained good results on average in comparison to standard iterated local search metaheuristic algorithms for the QAP.
|
Report
(4 results)
Research Products
(31 results)
