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Tang Huan-wen

Bio: Tang Huan-wen is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Backtracking line search & Imperialist competitive algorithm. The author has an hindex of 5, co-authored 7 publications receiving 225 citations.

Papers
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Journal ArticleDOI
TL;DR: Simulation results of typical complex function optimization show that CSA improves the convergence and is efficient, applicable and easy to implement.
Abstract: Simulated annealing (SA) has been applied with success to many numerical and combinatorial optimization problems in recent years. SA has a rather slow convergence rate, however, on some function optimization problems. In this paper, by introducing chaotic systems to simulated annealing, we propose a optimization algorithm named chaos simulated annealing (CSA). The distinctions between CSA and SA are chaotic initialization and chaotic sequences replacing the Gaussian distribution. Simulation results of typical complex function optimization show that CSA improves the convergence and is efficient, applicable and easy to implement. In addition, we discuss the advantages of CSA, and show the reasons why CSA performs better than SA.

202 citations

Journal ArticleDOI
TL;DR: A non-linear IO system is presented, based on the IO theory and production function theory, an extension of the famous Leontief’s IO model, which puts forward a new and more flexible adjustment equation of IO coefficients matrix.

14 citations

Journal Article
TL;DR: In this paper, an inventory model for finding the replenishment schedule for an inventory system, in which items deteriorate at a constant rate and demand is price-sensitive when the supplier gives quantity discounts.
Abstract: In this paper,we develop an inventory model for finding the replenishment schedule for an inventory system,in which items deteriorate at a constant rate and demand is price-sensitive when the supplier gives quantity discounts.It is shown that when the supplier gives quantity discounts,the buyer's demand increa-(ses).Sensitivity analysis with the numerical example shows that order quantity and replenishment cycle are very sensitive to the deterioration rate,but the retailer's profit per unit time is not.The retailer's unit selling price and profit per unit time are very sensitive to the price-sensitive parameter.A numerical example is presented.

7 citations

Journal ArticleDOI
TL;DR: The numerical results indicate that when the network synchronizability is improved, the geographical distance becomes larger while the maximal load decreases, indicating that the maximal betweenness can be a candidate factor that affects the network synchronization both in topological space and in geographical space.
Abstract: We investigate the relationship between the structure and the synchronizability of scale-free networks in geographical space. With an optimization approach, the numerical results indicate that when the network synchronizability is improved, the geographical distance becomes larger while the maximal load decreases. Thus the maximal betweenness can be a candidate factor that affects the network synchronizability both in topological space and in geographical space.

6 citations

Journal Article
TL;DR: A new line search algorithm for unconstrained optimization that obtains the step-length after doing finite operations and has the same theoretical characters as the famous Wolf-Powell line search method for the minimization of the functions which are twice continuously differentiable and bounded below.
Abstract: Based on the analysis for several kinds of line search methods,we propose a new line search algorithm for unconstrained optimization.It obtains the step-length after doing finite operations and has the same theoretical characters as the famous Wolf-Powell line search method for the minimization of the functions which are twice continuously differentiable and bounded below.Moreover,it requires two gradient evaluations at most in each of iteration and can save computation cost for the case when gradient evaluations are expensive.Numerical experiments show that the new algorithm in this paper is feasible and efficient.

5 citations


Cited by
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Journal ArticleDOI
Bo Liu1, Ling Wang1, Yihui Jin1, Fang Tang2, Dexian Huang1 
TL;DR: Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality.
Abstract: As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality.

879 citations

Journal ArticleDOI
TL;DR: This study introduces chaos into FA so as to increase its global search mobility for robust global optimization and shows that some chaotic FAs can clearly outperform the standard FA.

703 citations

Journal ArticleDOI
TL;DR: A comprehensive review of simulated annealing (SA)-based optimization algorithms, which solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima.
Abstract: This paper presents a comprehensive review of simulated annealing (SA)-based optimization algorithms. SA-based algorithms solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima. Three single objective optimization algorithms (SA, SA with tabu search and CSA) and five multiobjective optimization algorithms (SMOSA, UMOSA, PSA, WDMOSA and PDMOSA) based on SA have been presented. The algorithms are briefly discussed and are compared. The key step of SA is probability calculation, which involves building the annealing schedule. Annealing schedule is discussed briefly. Computational results and suggestions to improve the performance of SA-based multiobjective algorithms are presented. Finally, future research in the area of SA is suggested.

541 citations

Journal ArticleDOI
TL;DR: Chaos is introduced into Bat algorithm so as to increase its global search mobility for robust global optimization and results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.

445 citations

01 Jan 1988
TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
Abstract: In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of Rn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as originally given for discrete optimization problems. The mathematic formulation is extended to continuous optimization problems and we prove asymptotic convergence to the set of global optima. Furthermore, we discuss an implementation of the algorithm and compare its performance with other well known algorithms. The performance evaluation is carried out for a standard set of test functions from the literature. Keywords: global optimization, continuous variables, simulated annealing.

382 citations