Showing papers by "Gexiang Zhang published in 2008"
Journal Article•
TL;DR: Experimental results show that this evolutionary algorithm performs better than quantum-inspired evolutionary algorithms, for certain arrangements of the compartments of the P system structure utilized.
Abstract: This paper introduces an evolutionary algorithm which uses the concepts and principles of the quantum-inspired evolutionary approach and the hierarchical arrangement of the compartments of a P system. The P system framework is also used to formally specify this evolutionary algorithm. Extensive experiments are conducted on a well-known combinatorial optimization problem, the knapsack problem, to test the effectiveness of the approach. These experimental results show that this evolutionary algorithm performs better than quantum-inspired evolutionary algorithms, for certain arrangements of the compartments of the P system structure utilized.
(This work is supported by the National Natural Science Foundation of China (60702026, 60572143).)
111 citations
30 Dec 2008
TL;DR: Experimental results show that MArQ is superior to the real-observation quantum-inspired evolutionary algorithm and several optimization algorithms reported, in terms of search capability and stability.
Abstract: To enhance the local search capability of quantum-inspired evolutionary algorithm, a novel memetic algorithm based on real-observation quantum-inspired evolutionary algorithms (MArQ) was proposed. MArQ is a hybrid algorithm combining QIEA with local search techniques. In MArQ, QIEA was used to explore the whole solution space and tabu search was employed to exploit the neighboring domains of the searched best solutions. Several bench complex functions and an application example of reactive power optimization in power systems were applied to test the MArQ performances. Experimental results show that MArQ is superior to the real-observation quantum-inspired evolutionary algorithm and several optimization algorithms reported, in terms of search capability and stability.
11 citations
25 Jun 2008
TL;DR: The presented approach can successfully abstract the local frequency features and envelop features of every component of the multi-component LFM radar signal, and estimate the numbers and frequency offsets of component signal of the complex radar signal.
Abstract: Based on local wave decomposition (LWD), a novel approach for detecting the multi-component linear frequency modulated (LFM) radar emitter signal is proposed in this paper. Every complex radar signal is decomposed into its intrinsic mode components, meanwhile the signal local characteristics are dynamically depicted by instantaneous frequencies. The presented approach can successfully abstract the local frequency features and envelop features of every component of the multi-component LFM radar signal, and estimate the numbers and frequency offsets of component signal of the complex radar signal. Theoretical analysis and experimental results indicate that the proposed approach is effective to analyze and detect the multi-component LFM radar emitter signal.