Aijia Ouyang

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A hybrid particle swarm optimization (HPSO) algorithm, which combines the advantages of Nelder-Mead simplex method (SM) and particle swarm optimization (PSO) algorithm, is put forward to solve systems of nonlinear equations, and it can be used to overcome the difficulty in selecting good initial guess for SM and inaccuracy of PSO due to being easily trapped(More)
  • Aijia Ouyang
  • 2010 IEEE Fifth International Conference on Bio…
  • 2010
Precise Algorithms combining evolutionary algorithms and constraint-handling techniques have shown to be effective to solve constrained optimization problems during the past decade. This paper presents a hybrid immune PSO (HIA-PSO) algorithm with a feasibility-based rule which is employed in this paper to handle constraints in solving global nonlinear(More)
In order to accelerate the convergence and improve the calculation accuracy for parameter optimization of the Muskingum model, we propose a novel, adaptive hybrid particle swarm optimization (AHPSO) algorithm. With the decreasing of inertial weight factor proposed, this method can gradually converge to a global optimal with elite individuals obtained by(More)
As an effective and efficient way to provide computing resources and services to customers on demand, cloud computing has become more and more popular. From cloud service providers’ perspective, profit is one of the most important considerations, and it is mainly determined by the configuration of a cloud service platform under given market demand. However,(More)
This paper first introduces the fundamental principles of immune algorithm (IA), greedy algorithm (GA) and delete-cross operator (DO). Based on these basic algorithms, a hybrid immune algorithm (HIA) is constructed to solve the traveling salesman problem (TSP). HIA employs GA to initialize the routes of TSP and utilizes DO to delete routes of crossover.(More)
The Muskingum model is the most widely used and efficient method for flood routing in hydrologic engineering; however, the applications of this model still suffer from a lack of an efficient method for parameter estimation. Thus, in this paper, we present a hybrid particle swarm optimization (HPSO) to estimate the Muskingum model parameters by employing PSO(More)
Objectives: We propose a parallel hybrid particle swarm optimization (PHPSO) algorithm to reduce the computation cost because solving the one-dimensional (1D) heat conduction equation requires large computational cost which imposes a great challenge to both common hardware and software equipments. Background: Over the past few years, GPUs have quickly(More)
We present a hybrid face recognition algorithm which is based on the linear discriminant analysis (LDA) improved by a fusion technique and learning vector quantization (LVQ) in the paper. Firstly, the improved LDA is utilized to reduce the sample vector dimension, and then the LVQ classifier is used to recognize human faces. We perform intensive set of(More)