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A Hierarchical algorithm Based on Density Peaks Clustering and Ant Colony optimization for Traveling Salesman Problem

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IEEE ACCESS 2018年 6卷 38921-38933页

作者： Liao, Erchong Liu, Changan North China Elect Power Univ Sch Control & Comp Engn Baoding 071003 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China

This paper proposed a hierarchical hybrid algorithm for traveling salesman problem (TSP) according to the idea of divide-and-conquer. The TSP problem is decomposed into a few subproblems with small-scale nodes by density peaks clustering algorithm. Every subproblem is resolved by ant colony optimization algorithm, this is the lower layer. The center nodes of all subproblems constitute a new TSP problem, which forms the upper layer. All local tours of these subproblems are joined to generate the initial global tour in the same order that the center nodes are traversed in the upper layer TSP problem. Finally, the global tour is optimized by k-opt algorithms. Thirty benchmark instances taken from TSPLIB are divided into three groups on the basis of problem size: small-scale, large-scale, and very large-scale. The experimental result shows that the proposed algorithm can obtain the solutions with higher accuracy and stronger robustness, and significantly reduce runtime, especially for the very large-scale TSP problem.

关键词： Ant colony optimization density peaks clustering algorithm k-opt algorithm traveling salesman problem

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Research on Generalized Traveling Salesman Problem based on Modified Ant Colony optimization 31

Research on Generalized Traveling Salesman Problem based on ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Li Meng Yu Lin Song Qing Fang Wenjing Univ Jinan Coll Elect Engn Jinan 250022 Peoples R China

ISBN: (纸本)9781728101057

The generalized traveling salesman problem(GTSP) is a typical combinatorial optimization problem. Many practical problems involving the allocation and optimization of multiple tasks can be reduced to the generalized traveling salesman problem. A modified ant colony optimization is proposed for GTSP. The modified ant colony algorithm combines 2-opt algorithm to minimize the total path length, while considering the task balance of different travelers. Simulation results show that the modified ant colony optimization has good optimization accuracy and stability in solving the generalized traveling salesman problem.

关键词： Generalized Traveling Salesman Problem Ant Colony optimization k-opt algorithm MATLAB

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Design of optimal Frequency-Selective FIR Filters Using a Memetic algorithm 26

Design of Optimal Frequency-Selective FIR Filters Using a Me...

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European Signal Processing Conference (EUSIPCO)

作者： San-Jose-Revuelta, Luis M. Univ Valladolid Dept Signal Theory & Commun Valladolid Spain

ISBN: (纸本)9789082797015

Finite Impulse Response (FIR) digital filters are widely used due to their capabilities of low computational load, stability and linear phase. Most traditional design approaches can not control with enough accuracy the frequency response of the designed filter. For this reason, we propose the use of a memetic algorithm along with a weighted fitness function, to estimate optimized filter coefficients that best approximate ideal specifications. Results have been compared to both traditional methods (mainly windowing and the Parks-McClellan algorithm) as well as to several bio-inspired techniques. Numerical results show that proposed method achieves better fit to filter specifications, a larger attenuation in the stop band and a narrower transition band, at the expense of slightly increasing the pass-band ripple (0.5 - 0.7 dB), the later in about 68% of the cases.

关键词： FIR design memetic algorithm genetic algorithm k-opt algorithm

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A Novel Hybrid Path Planning algorithm at Macroscopic Level for Autonomous Climbing Robot 3

A Novel Hybrid Path Planning Algorithm at Macroscopic Level ...

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3rd International Conference on Robotics and Automation Engineering (ICRAE)

作者： Liao, Erchong North China Elect Power Univ Sch Control & Comp Engn Baoding Peoples R China

ISBN: (纸本)9781538695944

This paper proposed a novel hybrid algorithm for path planning at macroscopic level for autonomous climbing robot. The path planning is an Asymmetric Traveling Salesman Problem (ATSP). The problem can be decomposed into some groups with small-scale points by Density Peaks Clustering algorithm (DPC), and these groups are divided into two types based on the node distribution: dense groups and sparse groups. Then local paths for all groups are solved by Ant Colony optimization algorithm (ACO) and merged at the nearest pair of points between the two adjacent groups to generate the initial global path. At last, the final global path is obtained after optimizing by k-opt algorithms. The proposed algorithm is tested on nine benchmark instances compared with four other algorithms. Simulation experiment result shows that the proposed algorithm can provide the solution with higher accuracy and shorter runtime.

关键词： path planning climbing robot asymmetric traveling salesman problem density peaks clustering algorithm ant colony optimization algorithm k-opt algorithm

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