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Search Results: 1 - 10 of 34556 matches for " Yongquan Zhou "
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A class of irregular wavelet frames
Xingwei Zhou,Yongquan Li
Chinese Science Bulletin , 1997, DOI: 10.1007/BF02883048
An Effective Adaptive Multi-objective Particle Swarm for Multimodal Constrained Function Optimization
Yongquan Zhou,Shengyu Pei
Journal of Computers , 2010, DOI: 10.4304/jcp.5.8.1144-1151
Abstract: This paper presents a novel adaptive multi-objective particle swarm optimization algorithm and with adaptive multi-objective particle swarm algorithm for solving constrained function optimization problems, in which Pareto non-dominated ranking, tournament selection, crowding distance method were introduced, simultaneously the rate of crowding distance changing were integrated into the algorithm. Finally, ten standard functions are used to test the performance of the algorithm, experimental results show that the proposed approach is an effecient, and achieve a high-quality performance.
A Novel Numerical Computation Method Based on Particle Swarm Optimization Algorithm
Yongquan Zhou,Xingqiong Wei
Journal of Computers , 2010, DOI: 10.4304/jcp.5.2.226-233
Abstract: In this paper, a novel numerical computation method based on particle swarm optimization (PSO) was presented, including numerical integral, eigenvalues and eigenvectors of matrix, interpolation polynomial. Simulation results show that the algorithms are validated methods with high precision and powerful self-adapting. These algorithms have value in numerical calculation and engineering practice.
A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems
Yongquan Zhou,Shengyu Pei
Journal of Computers , 2010, DOI: 10.4304/jcp.5.6.965-972
Abstract: This paper presents an effective hybrid co-evolutionary particle swarm optimization algorithm for solving constrained engineering design problems, which is based on simulated annealing (SA) , employing the notion of co-evolution to adapt penalty factors. By employing the SA-based selection for the best position of particles and swarms when updating the velocity in co-evolutionary particle swarm optimization algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed, and can reach a high precision.
A Quasi-Newton Population Migration Algorithm for Solving Systems of Nonlinear Equations
Hongxia Liu,Yongquan Zhou,Yongmei Li
Journal of Computers , 2011, DOI: 10.4304/jcp.6.1.36-42
Abstract: In this paper, the problem on solving nonlinear equations is transformed into that of function optimization. A new Quasi-Newton Population Migration Algorithm (QPMA) is proposed via combination of population migration algorithm and Quasi-Newton method. The algorithm has the advantages of the Population Migration Algorithm (PMA) such as region search in a certain extent and avoid getting into the local optimum and the Quasi-Newton method such as Quasi-Newton’s local strong searching. Finally, the numerical experiments result show that this algorithm can find the rapid and effective interval solution and the probability of success is higher.
Population Migration Algorithm Description Method and Application Based on Unified Framework of Swarm Intelligence
Yongquan Zhou,Weiwei Zhang,Aijia Ouyang
Journal of Networks , 2010, DOI: 10.4304/jnw.5.4.427-434
Abstract: Population migration algorithm (PMA) is proposed in recent years, it’s a new search algorithm for global optimization that mainly simulates population transition with economics and dispersion with population pressure increment, the former encourages the algorithm to search in a region with good solutions, the latter avoids getting stuck in a local optimum to a certain degree. So far
A Novel Bat Algorithm Based on Differential Operator and Lévy Flights Trajectory
Jian Xie,Yongquan Zhou,Huan Chen
Computational Intelligence and Neuroscience , 2013, DOI: 10.1155/2013/453812
A Novel Bat Algorithm Based on Differential Operator and Lévy Flights Trajectory
Jian Xie,Yongquan Zhou,Huan Chen
Computational Intelligence and Neuroscience , 2013, DOI: 10.1155/2013/453812
Abstract: Aiming at the phenomenon of slow convergence rate and low accuracy of bat algorithm, a novel bat algorithm based on differential operator and Lévy flights trajectory is proposed. In this paper, a differential operator is introduced to accelerate the convergence speed of proposed algorithm, which is similar to mutation strategy “DE/best/2” in differential algorithm. Lévy flights trajectory can ensure the diversity of the population against premature convergence and make the algorithm effectively jump out of local minima. 14 typical benchmark functions and an instance of nonlinear equations are tested; the simulation results not only show that the proposed algorithm is feasible and effective, but also demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space. 1. Introduction Nowadays, since the evolutionary algorithm can solve some problem that the traditional optimization algorithm cannot do easy, the evolutionary algorithms are widely applied in different fields, such as the management science, engineering optimization, scientific computing. More and more modern metaheuristic algorithms inspired by nature or social phenomenon are emerging and they become increasingly popular, for example, particles swarms optimization (PSO) [1], firefly algorithm (FA) [2, 3], artificial chemical reaction optimization algorithm (ACROA) [4], glowworm swarms optimization (GSO) [5], invasive weed optimization (IWO) [6], differential evolution (DE) [7–9], bat algorithm (BA) [2, 10], and so on [11–15]. Some researchers have proposed their hybrid versions by combining two or more algorithms. Bat Algorithm (BA) is a novel metaheuristic optimization algorithm based on the echolocation behaviour of microbats, which was proposed by Yang in 2010 [2, 10]. This algorithm gradually aroused people’s close attention, and which is increasingly applied to different areas. Tsai et al. (2011) proposed an improved EBA to solve numerical optimization problems [16]. A multiobjective bat algorithm (MOBA) is proposed by Yang (2011) [17], which is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. In 2012, Bora et al. applied BA to solve the Brushless DC Wheel Motor Problem [18]. Although the basic BA has remarkable property compared against several traditional optimization methods, the phenomenon of slow convergence rate and low accuracy still exists. Therefore, in this paper, we put forward an improved bat algorithm based on differential operator and Lévy
A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems
Yongquan Zhou,Qifang Luo,Huan Chen
Journal of Applied Mathematics , 2013, DOI: 10.1155/2013/757391
Abstract: In view of the traditional numerical method to solve the nonlinear equations exist is sensitive to initial value and the higher accuracy of defects. This paper presents an invasive weed optimization (IWO) algorithm which has population diversity with the heuristic global search of differential evolution (DE) algorithm. In the iterative process, the global exploration ability of invasive weed optimization algorithm provides effective search area for differential evolution; at the same time, the heuristic search ability of differential evolution algorithm provides a reliable guide for invasive weed optimization. Based on the test of several typical nonlinear equations and a circle packing problem, the results show that the differential evolution invasive weed optimization (DEIWO) algorithm has a higher accuracy and speed of convergence, which is an efficient and feasible algorithm for solving nonlinear systems of equations. 1. Introduction Systems of nonlinear equations arise in many domains of practical importance such as engineering, mechanics, medicine, chemistry, and robotics. Solving such a system involves finding all the solutions (there are situations when more than one solution exists) of the polynomial equations contained in the mentioned system. The algorithms of solving nonlinear equations systems are worse than linear equations in convergence speed and ratio, especially solving nonconvex nonlinear equations. The traditional solutions of nonlinear equations include Newton-Raphson method, Quasi-Newton method, and homotopy method. Newton-Raphson method is a much more classical method, but it is sensitive to initial iteration value. In addition, not only it requires a large amount of calculation, but also sometimes it accompanied by difficulty calculation. Quasi-Newton method is to solve the difficult caused by Jacobi matrix. It has now become one of the most effective methods that solve nonlinear equations and optimization problems. While, its stability is poor and sometimes its iterative effect is not well. The basic idea of homotopy method is to start from easily solved equations and then gradually transit to the original equations and get the solution of problems. In recent years, with the rapid development of computational intelligence, computational intelligence techniques have also been used to solve nonlinear equations, such as genetic algorithm [1–3], particle swarm optimization algorithm [4], differential evolution algorithm [5], artificial fish-swarm algorithm [6], artificial bee colony algorithm [7] harmony search algorithm [8], and
Automata-Based Analysis of Stage Suspended Boom Systems
Anping He,Jinzhao Wu,Shihan Yang,Yongquan Zhou
Journal of Applied Mathematics , 2013, DOI: 10.1155/2013/739253
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