Simulation Study of Swarm Intelligence Based on Life Evolution Behavior
Swarm intelligence (SI) is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2015/291298 |
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| _version_ | 1832550066949193728 |
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| author | Yanmin Liu Ying Bi Changling Sui Yuanfeng Luo Zhuanzhou Zhang Rui Liu |
| author_facet | Yanmin Liu Ying Bi Changling Sui Yuanfeng Luo Zhuanzhou Zhang Rui Liu |
| author_sort | Yanmin Liu |
| collection | DOAJ |
| description | Swarm intelligence (SI) is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO) and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence. |
| format | Article |
| id | doaj-art-5e35b694c24e48be9f22adba4aa16e99 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-5e35b694c24e48be9f22adba4aa16e992025-02-03T06:07:48ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/291298291298Simulation Study of Swarm Intelligence Based on Life Evolution BehaviorYanmin Liu0Ying Bi1Changling Sui2Yuanfeng Luo3Zhuanzhou Zhang4Rui Liu5School of Mathematics and Computer Science, Zunyi Normal College, Zunyi 563002, ChinaCollege of Management, Shenzhen University, Shenzhen 518060, ChinaCollege of Life Science, Zunyi Normal College, Zunyi 563002, ChinaSchool of Mathematics and Computer Science, Zunyi Normal College, Zunyi 563002, ChinaSchool of Mathematics and Computer Science, Zunyi Normal College, Zunyi 563002, ChinaSchool of Mathematics and Computer Science, Zunyi Normal College, Zunyi 563002, ChinaSwarm intelligence (SI) is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO) and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence.http://dx.doi.org/10.1155/2015/291298 |
| spellingShingle | Yanmin Liu Ying Bi Changling Sui Yuanfeng Luo Zhuanzhou Zhang Rui Liu Simulation Study of Swarm Intelligence Based on Life Evolution Behavior Discrete Dynamics in Nature and Society |
| title | Simulation Study of Swarm Intelligence Based on Life Evolution Behavior |
| title_full | Simulation Study of Swarm Intelligence Based on Life Evolution Behavior |
| title_fullStr | Simulation Study of Swarm Intelligence Based on Life Evolution Behavior |
| title_full_unstemmed | Simulation Study of Swarm Intelligence Based on Life Evolution Behavior |
| title_short | Simulation Study of Swarm Intelligence Based on Life Evolution Behavior |
| title_sort | simulation study of swarm intelligence based on life evolution behavior |
| url | http://dx.doi.org/10.1155/2015/291298 |
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