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: Yanmin Liu, Ying Bi, Changling Sui, Yuanfeng Luo, Zhuanzhou Zhang, Rui Liu
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2015/291298
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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.
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institution Kabale University
issn 1026-0226
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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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AT yingbi simulationstudyofswarmintelligencebasedonlifeevolutionbehavior
AT changlingsui simulationstudyofswarmintelligencebasedonlifeevolutionbehavior
AT yuanfengluo simulationstudyofswarmintelligencebasedonlifeevolutionbehavior
AT zhuanzhouzhang simulationstudyofswarmintelligencebasedonlifeevolutionbehavior
AT ruiliu simulationstudyofswarmintelligencebasedonlifeevolutionbehavior