Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational comp...Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid ap- proximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimiza- tion (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.展开更多
Attitude determination is a key technology in aerospace, sailing and land-navigation etc. In the method of double difference phase measurement, it is a crucial topic to solve the carrier phase integer ambiguity, which...Attitude determination is a key technology in aerospace, sailing and land-navigation etc. In the method of double difference phase measurement, it is a crucial topic to solve the carrier phase integer ambiguity, which is shown to be a combination optimization problem, and thus efficient heuristic algorithms are needed. In this paper, we propose a discrete particle swarm optimization (DPSO)-based solution which aims at searching for the optimal integer ambiguity directly without decorrelation of ambiguity, and computing the baseline vector consequently. A novel flat binary particle encoding approach and corresponding revision operation are presented. Furthermore, domain knowledge is incorporated to significantly improve the convergence rate. Through extensive experiments, we demonstrate that the proposed algorithm outperforms a classic algorithm by up to 80% in time efficiency with solution quality guaranteed. The experiment results show that this algorithm is efficient, robust, and suitable for dynamic attitude determination.展开更多
The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existi...The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.展开更多
基金Project (No. 60174009) supported by the National Natural ScienceFoundation of China
文摘Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid ap- proximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimiza- tion (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.
基金supported by the National Research Foundation for the Doctoral Program of Higher Education New Teacher of China (Grant No. 20070359029)the Natural Science Foundation of Anhui Province of China (Grant No. 070412035)
文摘Attitude determination is a key technology in aerospace, sailing and land-navigation etc. In the method of double difference phase measurement, it is a crucial topic to solve the carrier phase integer ambiguity, which is shown to be a combination optimization problem, and thus efficient heuristic algorithms are needed. In this paper, we propose a discrete particle swarm optimization (DPSO)-based solution which aims at searching for the optimal integer ambiguity directly without decorrelation of ambiguity, and computing the baseline vector consequently. A novel flat binary particle encoding approach and corresponding revision operation are presented. Furthermore, domain knowledge is incorporated to significantly improve the convergence rate. Through extensive experiments, we demonstrate that the proposed algorithm outperforms a classic algorithm by up to 80% in time efficiency with solution quality guaranteed. The experiment results show that this algorithm is efficient, robust, and suitable for dynamic attitude determination.
基金Supported by the National Natural Science Foundation of China (No. 90818007)
文摘The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.
