作者机构：
[Tan Guan-zheng; Tan Yue] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China.;[Tan Yue; Deng Shu-guang] Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Peoples R China.
通讯机构：
[Tan Guan-zheng] C;Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China.
关键词：
particle swarm optimization;chaotic search;integer programming problem;mixed integer programming problem
摘要：
A novel chaotic search method is proposed, and a hybrid algorithm combining particle swarm optimization (PSO) with this new method, called CLSPSO, is put forward to solve 14 integer and mixed integer programming problems. The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods. Experimental results indicate that in terms of robustness and final convergence speed, CLSPSO is better than other five algorithms in solving many of these problems. Furthermore, CLSPSO exhibits good performance in solving two high-dimensional problems, and it finds better solutions than the known ones. A performance index (PI) is introduced to fairly compare the above six algorithms, and the obtained values of (PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.
摘要：
In this letter, we propose a method for measuring the pulsewidths at different spatial positions of ultrashort laser pulses. By measuring the pulsewidths varying with spatial positions of chirped and chirped-free femtosecond laser pulses, it is found that pulsewidths at edge positions are longer than that of central positions due to the effect of residual spatial chirp. Then, we measure the temporal evolutions of pulse at the strongest spatial modulation position after small-scale self-focusing and our results show that the pulsewidths become narrower with increasing spatial contrast due to spatiotemporal coupling effect. We find that the method is reliable and feasible.
作者机构：
[Tan Guan-zheng; Tan Yue] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.;[Tan Yue; Deng Shu-guang] Hunan City Univ, Sch Commun & Elect Engn, Yiyang 413000, Peoples R China.
通讯机构：
[Tan Guan-zheng] C;Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.
关键词：
particle swarm optimization;differential evolution;chaotic local search;reliability-redundancy allocation
摘要：
In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search). And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics, and CDEPSO algorithm is the best in solving these problems.
作者机构：
[沈连丰; 邓曙光; 李俊超] National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China;[邓曙光] Department of Physics and Telecommunication Engineering, Hunan City University, Yiyang 413000, China
通讯机构：
[Shen, L.] N;National Mobile Communications Research Laboratory, Southeast University, China
