Guijun Zhang

Ant colony pattern search algorithms for unconstrained and bound constrained optimization

Abstract Ant colony optimization is a class of metaheuristics which succeed in NP-hard combinational optimization problems rather than continuous optimization problems. We present and analyze a class of ant colony algorithms for unconstrained and bound constrained optimization on R n : Ant Colony Pattern Search Algorithms (APSAs). APSAs use the ant colony framework guided by objective function heuristic pheromone to perform local searches, whereas global search is handled by pattern search algorithms. The analysis results of APSAs prove that they have a probabilistic, weak stationary point convergence theory. APSAs present interesting emergent properties as it was shown through some analytical test functions.

Modeling and Implementing Research of Multimodal Transportation Network

To satisfy the needs of a variety of demand-responsive transport, the multimodal transportation problem appeared. This paper focuses on network modeling with a hierarchical structure. The relationship between different levels is described in detail, and dynamic segmentation and linear referencing techniques are used to solve the overlay problem in multimodal network. Furthermore, a shortest path algorithm is proposed to solve the transfer problem with several public vehicle modes. Finally, the results simulated by Geographic Information System (GIS) are given to demonstrate the effectiveness of the proposed model and algorithm.

Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution

De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.

Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction

Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE.