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Research and Application for Swarm Intelligent Fusion Algorithm

Author: LiJun
Tutor: SunHui
School: Nanchang University of Aeronautics and
Course: Applied Computer Technology
Keywords: Particle Swarm Optimization hybrid algorithm Shuffled Frog Leaping Algorithm artificial bee colony algorithm Wireless Sensor
CLC: TP301.6
Type: Master's thesis
Year: 2013
Downloads: 59
Quote: 0
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Abstract


Swarm intelligence refers to a kind of problem-solving ability that emerges in the interactions of simple information-processing units. Many scholars, at home or abroad, have brought up lots of swarm intelligence optimization algorithm according to the different problems, such as particle swarm optimization, ant colony algorithm, shuffled frog leaping algorithm and artificial bee colony algorithm.Each swarm intelligence optimization algorithm has its own characteristics and advantages, as well as its weaknesses and shortcomings. The fusion of intelligent algorithm uses the complementarity in the three ways which are different optimization mechanism, optimization behavior and optimize the structure. it uses each other’s strengths to compensate for each other’s shortcomings, and to improve the performance of the solution of algorithm. Experiments show that existing swarm intelligence fusion algorithm has been to solve the premature issue in the single intelligent algorithm to some extent, and has been effectively solved many practical problems that a single intelligence algorithm is difficult to solve.First, three kinds of classic swarm intelligence optimization algorithms’principle, process and parameters are studied in the paper. Based on the advantages and disadvantages of these algorithms, we propose two new fusion algorithms and a modified particle swarm optimization. Second, the wireless sensor network coverage optimization problems are introduced. Finally, fusion algorithms are applied to the wireless sensor network coverage optimization problem. Specific innovation points and research contents are as follows:(1)This paper combines particle swarm optimization algorithm and artificial bee colony algorithm to propose a new algorithm which improve the global convergence performance of the algorithm. The algorithm uses multiswarm particle swarm optimization, and after each evolution, groups the best particles in the sub-swarms into a new group and uses artificial bee colony algorithm to evolve it. In the evolutionary model of each sub-swarm, in addition to considering the best particle of the sub-swarm, the best particle of the whole swarm is also considered. Compared with some improved particle swarm optimization or artificial bee colony algorithm, the hybrid algorithm is simple in concept, easy to implement, has a good global search capability and faster convergence speed.(2)A new particle swarm optimization algorithm is proposed, which can get rid of the shortcoming of particle Swarm optimization being easy to fall into local optimum in high-dimensional complex function optimization. The algorithm uses multiswarm particle swarm optimization. In the evolution process, in order to improve the global search ability, each subgroup of the optimal particle are sorted, then substituted the original optimal particles to share information. To accurate, quickly approach the global minimum, an elitist learning strategy is developed for the best particle of the whole swarm. After that, in the evolutionary model of each sub-swarm, in order to accelerating convergence speed, the best particle of the whole swarm is also considered.(3)We propose a new fusion algorithm based on particle Swarm optimization and shuffled frog leaping algorithm. The new fusion algorithm’s principle build on the same principle as the fusion algorithm which combines particle swarm optimization algorithm and artificial bee colony algorithm, but the difference is to use artificial bee colony to fusion algorithm instead shuffled frog leaping algorithm. Then it applied to wireless sensor distribution problem, Compared with standard particle swarm optimization or standard artificial bee colony algorithm, the hybrid algorithm can make a more uniform distribution of wireless sensor nodes and higher coverage of the networks.

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