Solving a kind of high complexity multi-objective problems by a fast algorithm

A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions. Foundation item: Supported by the National Natural Science Foundation of China (60204001, 70071042, 60073043, 60133010) and Youth Chengguang Project of Science and Technology of Wuhan City (20025001002). Biography: Zeng San-you ( 1963-), male, Associate professor, research direction: evolutionary computing, parallel computing     

Asynchronous parallel evolutionary algorithms for constrained optimizations

Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm. National Laboratory for Parallel and Distributed Processing Foundation item: Supported by the Natonal Natural Science Foundation of China (No. 70071042, 50073043), the National 863 Hi-Tech Project of China (No. 863-306-ZT06-06-3) and the National Laboratory for Parallel and Distributed Processing. Biography: Kang Li-shan (1934-), male, Professor, research interests: parallel computing and evolutionary computation.     

Numerical integration method in analysis of wire antennas

The numerical evaluation of an integral is a frequently encountered problem in antenna analysis. A special Gauss-Christoffel quadrature formula for nonclassical weight function is constructed for solving the pseudo-singular integration problem arising from the analysis of thin-wire antennas. High integration accuracy is obtained at comparable low computation cost by the quadrature formula constructed. This integration method can be also used in other electromagnetic integral equation problems. Supported by the 863 High Technology Project of China (No. 863-818-01-02) and the National Science Foundation of Hubei Province (No. 97J002) Yang Shaolin: born in 1975, M. S graduate student.     

A genetic algorithm for the flowshop scheduling problem

The flowshop scheduling problem is NP complete. To solve it by genetic algorithm, an efficient crossover operator is designed. Compared with another crossover operator, this one often finds a better solution within the same time. Supported by the National Natural Science Foundation of China and 863 High Technology Project of China Qi Yuesheng: born in 1967, Ph. D.     

Balance between exploration and exploitation in genetic search

Genetic search plays an important role in Evolutionary Computation (EC). There are two important issues in the evolution process of the genetic search: exploration and exploitation. Exploration is the creation of population diversity by exploring the search space; exploitation is the reduction of the diversity by focusing on the individuals of higher fitness, or exploiting the fitness information (or knowledge) represented within the population. We theoretically analyze the impact of the genetic operators on the balance. In order to further explain the impact, some results of our research on ESs are showed. Finally we conclude that to make the algorithm more efficient, it is important to strike a balance between these two factors. Supported in part by the National Natural Science Foundation of China (No. 69635030) and Doctoral Programme Foundation of China, and National 863 High Technology Project of China Lin Hansheng: born in 1975, Master     

Secure localization algorithm based on node cooperative for sensor networks

As to the safety threats faced by sensor networks （SN）, nodes limitations of computation, memory and communication, a secure location algorithm （node cooperative secure localization, NCSL） is presented in this paper. The algorithm takes the improvements of SN location information security as its design targets, utilizing nodes＇ cooperation to build virtual antennae array to communicate and localize, and gains arraying antenna advantage for SN without extra hardware cost, such as reducing multi-path effects, increasing receivers＇ signal to noise ratio and system capa- bility, reducing transmitting power, and so on. Simulations show that the algorithm based on virtual antennae array has good localization ability with a at high accuracy in direction-of-arrival （DOA） estimation, and makes SN capable to resist common malicious attacks, especially wormhole attack, by using the judgment rules for malicious attacks.     

The harmonic decomposition reconstruction for the exponential radon transform

The exponential Radon transform, a generalization of the Radon transform, is defined and studied as a mapping of function spaces. It is represented in terms of Fourier transform of its domain and range, and this leads to the harmonic decomposition reconstruction. The results are similar results of Tretiak and Metz. Foundation item: Supported by the National Natural Science Foundation of China (No. 19971064), Key Project of Science and Technology of Hubei Province Education Committee. Biography: Wang Jin-ping (1963-), male, Ph.D. candidate, research direction: numberical solution of singular integral equation and integral transformation etc.     

Using evolutionary computation to solve problems in nonparametric regression

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building. Supported by the National Natural Science Foundation and 863 High Technology Project of China Ding Lixin: born in 1967, Ph.D. graduate student     

A two-level subspace evolutionary algorithm for solving multi-modal function optimization problems

In this paper, a new algorithm for solving multimodal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043, 60133010). Biography: Li Yan( 1974-), female, Ph. D candidate, research interest: evolutionary computation.     

Using evolutionary computation to solve problems in nonparametric regression

This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building. Supported by the National Natural Science Foundation and 863 High Technology Project of China Ding Lixin: born in 1967, Ph.D. graduate student