Bio-Inspired Algorithm for the Shortest Path According to the Maximum Time for Each Trial

Seung Gwan Lee; Seung Won Lee

doi:10.4028/www.scientific.net/AMR.717.455

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A Novel Hybrid Optimization Method of Shuffled Frog Leaping Algorithm and Particle Swarm Optimization
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Emotion Intelligence Computation Based on Matrix Description of State Machine
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Research and Implementation of Vehicle-Logo Recognition Based on Modified Invariant Moments
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Quantitative Models for Computing Distance and Directions: A Raster-Based Approach
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Bio-Inspired Algorithm for the Shortest Path According to the Maximum Time for Each Trial
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Multi-Objective Optimization of Order Oriented Mixed-Model Assembly Line Sequencing Problem
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A Priority-Based Weighted Inner Products Matching Coarsening Algorithm on Multilevel Hypergraph Partitioning
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A Multi-Language Speech Recognition Method Based on Confidence Bayesian Decision-Making
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The Application of Matrix Partial Order in the Comparison of Linear Model
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 717Bio-Inspired Algorithm for the Shortest Path...

Bio-Inspired Algorithm for the Shortest Path According to the Maximum Time for Each Trial

Abstract:

Ant Colony System (ACS) is a new meta heuristics algorithms to solve hard combinatorial optimization problems. In this paper, we propose hybrid ant colony algotirhm that is searching the second best edge first in the state transition rule and updating the pheromone on edges applying the visited number of edge in the globally best tour. And we evaluate the proposed algorithm according to the maximum time for each trial. The results of a simulation experiment demonstrate that the proposed algorithm is better than, or, at least as good as, that of ACS algorithm in the most sets.

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Periodical:

Advanced Materials Research (Volume 717)

Pages:

455-459

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.717.455

Citation:

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Online since:

July 2013

Authors:

Seung Gwan Lee, Seung Won Lee

Keywords:

Ant Colony System (ACS), Combinatorial Optimization, Traveling Salesman Problem (TSP)

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