Flexible Protein Folding by Ant Colony Optimization

Hu, Xiao Min; Zhang, Mengjie; Li, Yun

doi:10.1007/978-3-540-70778-3_13

Cited by 8 publications

(3 citation statements)

References 47 publications

(51 reference statements)

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“…For the experimental evaluation, they use proteins that are based on the bibliography and some of them come from PDB. Hu et al [113] develop four different mechanismsto improve ACO algorithm, concretelyincludinga path retrieval method, the path construction, some folding heuristics and the pheromone attraction. These new mechanisms provide interesting results for solving protein folding problems with the HP square lattice model.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Llanes¹,

Muñoz²,

Bueno-Crespo³

et al. 2016

CDT

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The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.

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Section: Ant Colony Optimizationmentioning

confidence: 99%

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Llanes¹,

Muñoz²,

Bueno-Crespo³

et al. 2016

CDT

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show abstract

“…Researchers have been successfully applied to a wide range of application problems. The basic framework for ACO includes [13]:…”

Section: B Ant Colony Systemmentioning

confidence: 99%

An Improved Ant Colony Optimization for the Vehicle Routing Problem in Logistics Distribution

2010

AMR

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The routing of a fleet of vehicles to service a set of customers is important in logistic distribution systems. The main objective of Vehicle routing problem (VRP) is to minimize the total required fleet size for serving all customers. Secondary objectives are to minimize the total distance traveled or to minimize the total route duration of all vehicles. In this paper, we present a hybrid ant colony System, named PACS, coupled with a pareto local search (PLS) algorithm and apply to the VRP and its variant, the VRP with Time Windows (VRPTW). The algorithm only chooses partial customers randomly to compute the transition probability and PLS can help to escape local optimum. Experiments on various aspects of the algorithm and computational results for some benchmark problems are reported. We compare our approach with some classic, powerful meta-heuristics and show that the proposed approach can obtain the better quality of the solutions.

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“…The first ACO algorithm, called ant system [1], is designed for discrete problems like traveling salesman problem. Starting from Ant System, several improvements of the basic algorithm has been proposed [2,3]. Typically, these improved algorithms have been tested again on the TSP.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization with Local Search for Continuous Functions

2011

AMR

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Ant algorithms are a recently developed, population-based approach which was inspired by the observation of the behavior of ant colonies. Based on the ant colony optimization idea, we present a hybrid ant colony system (ACS) coupled with a pareto local search (PLS) algorithm, named PACS, and apply to the continuous functions optimization. The ACS makes firstly variable range into grid. In local search, we use the PLS to escape local optimum. Computational results for some benchmark problems demonstrate that the proposed approach has the high search superior solution ability.

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Flexible Protein Folding by Ant Colony Optimization

Cited by 8 publications

References 47 publications

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

An Improved Ant Colony Optimization for the Vehicle Routing Problem in Logistics Distribution

Ant Colony Optimization with Local Search for Continuous Functions

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