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Multi‐objective optimization algorithm of adaptive entropy wireless network based on embedded DSP
Author(s) -
Sun Lingfan,
Gui Lin
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4653
Subject(s) - computer science , mathematical optimization , maximization , optimization problem , entropy (arrow of time) , grasp , unavailability , scheduling (production processes) , energy consumption , algorithm , engineering , mathematics , electrical engineering , physics , quantum mechanics , reliability engineering , programming language
Summary The dynamic changes and uncertainties of wireless networks bring a lot of difficulties to the target management and task scheduling. In order to accurately grasp the target dynamic optimization and available capacity information, a new algorithm based on adaptive entropy and multi‐objective optimization is proposed. In adaptive entropy, the objective function and constraint condition of the maximum entropy and entropy increasing principle are used to select the objective which satisfies the maximization of the system, and the optimal scheduling is carried out, and then the multi‐objective optimization is carried out on the selected target, to migrate, release the target of heavy optimization and long‐term unavailability, which can reduce the energy consumption, realize the optimization balance, and improve the system utilization rate. The simulation experiment is designed to verify the performance of the proposed algorithm. The experimental results show that the adaptive entropy algorithm is very effective for user QoS and system maximization, which enhances the system utilization rate. This optimization algorithm has realized the user QoS guarantee, reduces the energy consumption, the optimized equilibrium, the enhanced system utilization, and so on many optimization goals.