Particle Swarm Optimization by Improved Chaos Immune Genetic Algorithm

Shao Song Wan; Jian Cao; Qun Song Zhu

doi:10.4028/www.scientific.net/AMR.989-994.2621

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Moving Vehicle Detection Scheme Using Edge Information and Background Subtraction in YCbCr Color Space
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A Hybrid 2-Population Genetic Algorithm for the Job Shop Scheduling Problem
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Robust Principal Component Analysis Algorithm Based on Nonparametric Correlation Coefficient Matrix
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Design Modeling and Simulation by Interpolation Algorithm of Zadoff-Chu Sequence
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Particle Swarm Optimization by Improved Chaos Immune Genetic Algorithm
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Construction of One-Lee Weight Codes over F_P+vF_P+v²F_P
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Dynamic Network Model Analysis Based on Communication Network
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Particle Swarm Optimization by Improved Chaos...

Particle Swarm Optimization by Improved Chaos Immune Genetic Algorithm

Abstract:

In order to resolve these problems, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems; it easily falls into local optima when solving complex multimodal problems. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. The paper gives the circuit diagram of the hardware components based on single chip and describe how to design the software. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

2621-2624

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.2621

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Online since:

July 2014

Authors:

Shao Song Wan*, Jian Cao, Qun Song Zhu

Keywords:

Algorithm (GA), Mutation, Structure, Swarm Intelligence

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