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Hybrid Intelligent Optimal Algorithms with Applications

Author: JiangFei
Tutor: LiuSanYang
School: Xi'an University of Electronic Science and Technology
Course: Applied Mathematics
Keywords: Artificial bee colony algorithm Stochastic gradient search Chaotic system Differential evolution algorithm Bacterial foraging algorithm Mutation operator
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 393
Quote: 1
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Abstract


Intelligent optimization algorithm uses group information exchange between individuals and co-operation to achieve the purpose of optimization, with a conceptually simple and easy to achieve higher efficiency and other characteristics, in many practical optimization problems has succeeded. Currently, intelligent optimization algorithms have been optimization algorithm to become a hot topic in the field of paper from the intelligent optimization algorithm and application of two aspects of the study. Firstly, improved artificial bee colony algorithm for solving single-objective optimization problem. Artificial bee colony algorithm is proposed in recent years A new swarm intelligence optimization algorithm, which controls less parameters, solving multimodal, high-dimensional optimization problem obviously. Based on the original artificial bee colony algorithm based on improved mining bees and bee observed search mechanism, the main manifested in: (a) introduced in the mining bees stochastic gradient search method to enhance its local search ability, improve search efficiency; (2) the differential variation observed bee drawing ideas, we propose a new search method to enhance global search ability to adopt and artificial bee colony, differential evolution, particle swarm compare numerical experiments indicate that the improved artificial bee colony algorithm for solving high precision, fast convergence and robustness, better than other three algorithms for solving Performance. Secondly, through analysis of several more classic multi-objective optimization problems evolutionary algorithm, we propose a new hybrid evolutionary algorithm using not only dominate the new algorithm is defined fitness function, crowding operator to maintain the diversity of solutions , but the idea of ??using the archive to accelerate the convergence speed to ensure the elite solution will not be lost when the target number of dimensions increases, between the non-dominated solutions by k-nearest neighbor operator to select from large individual retained. Numerical experiments show that the algorithm improves the solution speed, but also to ensure a uniform distribution of non-dominated solutions of last discussed the chaotic system control and synchronization problems. Firstly the problem into the function optimization problems; secondly proposes a new intelligent algorithm-CDEM algorithm that the differential evolution algorithm and bacterial foraging algorithm integration, improve the convergence of differential evolution and genetic mutation operator to maintain diversity of population and improve search efficiency; Finally CDEM typical algorithm Hénon Map chaotic system control and synchronization problems. Numerical experiments show that, CDEM algorithm not only can effectively solve the problem, but good stability. Moreover, the algorithm also analyzed CDEM various parameters affect the results.

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