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Ant Colony Optimization and Its Application

Author: HeXueHai
Tutor: HuXiaoBing
School: Chongqing University
Course: Computational Mathematics
Keywords: Ant Colony Algorithm Portfolio Optimization Metaheuristic Traveling Salesman Problem Adaptive transition probability
CLC: TP301.6
Type: Master's thesis
Year: 2011
Downloads: 115
Quote: 2
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Abstract


Combinatorial optimization problems in theory and practical applications have a very important position. With the expansion of the problem , because the computational complexity of the problem , if you use a lot of combinations of deterministic algorithms the optimal solution can not be achieved . ACO metaheuristic is a specialized intractable discrete optimization problems for the ideal way to get it within a reasonable period of time acceptable solution . ACO has a positive feedback, distributed , robust , easy to combine with other algorithms advantages . Theoretically , after appropriate transformation , ant colony optimization algorithm can solve any combinatorial optimization problem . Ant system from the initial development to the present multitude of improved ant colony algorithm, ant colony algorithm performance has made ??great progress. Already covered by their application portfolio optimization , continuous function optimization, network routing , machine learning , image processing , and many other disciplines. This paper mainly focus on the principle of ant colony optimization , the basic ant colony algorithm , ant colony optimization theory . On how to improve the performance of ant colony algorithm , how to improve the global search ability of in-depth research . Also summarized ACO ACO application rules and the use of the general steps to solve the problem , and finally gives some typical application examples. The main results are as follows : An improved ant colony algorithm, the transition probability formula introduces a new algorithm for adaptive factor in order to avoid falling into local optimal solution. With the increase in the number of iterations of the factors conducive to ant pheromone explore weaker side to avoid excessive accumulation of pheromone . This feature enables the ants still late in the iterative search for a higher probability of a better solution . Simulation results show that the improved algorithm to solve the traveling salesman problem has better global search capability .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
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