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Study on Power Optimal Allocation Based on Hybrid Genetic Algorithm

Author: JiangPing
Tutor: TangTianBing; LiMaoFeng
School: Guangxi University
Course: Computer technology
Keywords: collaborative communication power allocation geneticalgorithm complex niching
CLC: TP18
Type: Master's thesis
Year: 2012
Downloads: 13
Quote: 0
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Abstract


Wireless communications applications has penetrated every aspect of life, such that with a wide range of practical value of the wireless collaborative communication research. Power allocation problem is a basic problem of collaborative communication, and many scholars are studying on it. But bad thing is that current existence of algorithms is not ideal for its NP-hard characteristic. The genetic algorithm is an efficient intelligent algorithm, and it’s suitable for solving NP-hard problem. The program is its premature convergence and vulnerability of local search ability which badly affect the solving effect.To improve the quality of genetic algorithm for solving the power allocation problem, this paper solved the premature convergence problem of genetic algorithm by using niche strategy, then improved the local search capability through introducing complex method into the original algorithm such that constructing a high-performance hybrid genetic algorithm with the balance of breadth and depth of search. For power allocation problem, we designed a suitable choice, crossover, mutation, complex operator and niche operation. At the end of the algorithm, the best individual will stand for optimal power allocation scheme of our problem.At last, this article designed the collaborative network lifetime expedition algorithm based on the design of mixed genetic algorithm, considering energy-constrained. The experimental results show that comparing to the existing algorithms, the proposed algorithm has a greater advantage, and it does not only extend the collaboration network life effectively, but also performs a better stability with small fluctuation range for power allocation. This shows the effectiveness of the proposed algorithm.

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