A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization
Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/214814 |
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| author | Chih-Hao Lin Jiun-De He |
| author_facet | Chih-Hao Lin Jiun-De He |
| author_sort | Chih-Hao Lin |
| collection | DOAJ |
| description | Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems. |
| format | Article |
| id | doaj-art-6f703a0dd20a418e8a7f9f39db0d68f7 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-6f703a0dd20a418e8a7f9f39db0d68f72025-08-20T03:34:05ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/214814214814A Mutual-Evaluation Genetic Algorithm for Numerical and Routing OptimizationChih-Hao Lin0Jiun-De He1Department of Information Management, Chung Yuan Christian University, Jhongli 320, TaiwanDepartment of Information Management, Chung Yuan Christian University, Jhongli 320, TaiwanMany real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems.http://dx.doi.org/10.1155/2013/214814 |
| spellingShingle | Chih-Hao Lin Jiun-De He A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization Journal of Applied Mathematics |
| title | A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization |
| title_full | A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization |
| title_fullStr | A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization |
| title_full_unstemmed | A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization |
| title_short | A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization |
| title_sort | mutual evaluation genetic algorithm for numerical and routing optimization |
| url | http://dx.doi.org/10.1155/2013/214814 |
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