Abstract
Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization approaches employed is fast convergence. Usefulness of heuristic algorithm as the search method for diverse optimization problems is examined. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of three evolutionary-based algorithms: genetic algorithm, clonal selection algorithm and particle swarm optimization. A brief description of each algorithm is presented. Benchmark comparisons among these algorithms are presented optimization problems, in terms of processing time, convergence speed, and quality of the results. The simulation results show that compared with genetic algorithm and clonal selection algorithm, the proposed particle swarm optimization based algorithm can improve the quality of the solution while speeding up the convergence process. Three words can summarize the main features of the proposed approach: faster, cheaper, and simpler.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 |
| Pages | 879-884 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2008 Dec 29 |
| Event | 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 - Busan, Korea, Republic of Duration: 2008 Nov 11 → 2008 Nov 13 |
Publication series
| Name | Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 |
|---|---|
| Volume | 2 |
Other
| Other | 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 |
|---|---|
| Country | Korea, Republic of |
| City | Busan |
| Period | 08-11-11 → 08-11-13 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Software
Cite this
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Introduction and comparison of three evolutionary-based intelligent algorithms for optimal design. / Shyr, Wen Jye.
Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008. 2008. p. 879-884 4682357 (Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008; Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Introduction and comparison of three evolutionary-based intelligent algorithms for optimal design
AU - Shyr, Wen Jye
PY - 2008/12/29
Y1 - 2008/12/29
N2 - Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization approaches employed is fast convergence. Usefulness of heuristic algorithm as the search method for diverse optimization problems is examined. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of three evolutionary-based algorithms: genetic algorithm, clonal selection algorithm and particle swarm optimization. A brief description of each algorithm is presented. Benchmark comparisons among these algorithms are presented optimization problems, in terms of processing time, convergence speed, and quality of the results. The simulation results show that compared with genetic algorithm and clonal selection algorithm, the proposed particle swarm optimization based algorithm can improve the quality of the solution while speeding up the convergence process. Three words can summarize the main features of the proposed approach: faster, cheaper, and simpler.
AB - Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization approaches employed is fast convergence. Usefulness of heuristic algorithm as the search method for diverse optimization problems is examined. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of three evolutionary-based algorithms: genetic algorithm, clonal selection algorithm and particle swarm optimization. A brief description of each algorithm is presented. Benchmark comparisons among these algorithms are presented optimization problems, in terms of processing time, convergence speed, and quality of the results. The simulation results show that compared with genetic algorithm and clonal selection algorithm, the proposed particle swarm optimization based algorithm can improve the quality of the solution while speeding up the convergence process. Three words can summarize the main features of the proposed approach: faster, cheaper, and simpler.
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U2 - 10.1109/ICCIT.2008.76
DO - 10.1109/ICCIT.2008.76
M3 - Conference contribution
AN - SCOPUS:57849112728
SN - 9780769534077
T3 - Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
SP - 879
EP - 884
BT - Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
ER -