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Improvement and Application of Ant Colony Optimization

Author: ZhaoChaoQing
Tutor: HuXiaoBing
School: Chongqing University
Course: Operational Research and Cybernetics
Keywords: Ant Colony Optimization Immune Clone Algorithm Contrast Enhancement 0-1 knapsack problem
CLC: O224
Type: Master's thesis
Year: 2008
Downloads: 253
Quote: 2
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Abstract


The Italian scholar M.Dorigo, V.Maniezzo and A.Colorni in 1992 through the simulation ant colony foraging behavior of a population - based evolutionary algorithm - ant colony optimization . The algorithm proposed has aroused great attention from scholars , in just ten years time , combinatorial optimization , network routing , function optimization , data mining , robot path planning in areas such as access to a wide range of applications , and achieved good results. This paper focuses on ant colony algorithm and its application on how to improve the ant colony algorithm , mixed with other algorithms , and conducted in-depth research in the field of combinatorial optimization applications . The article's main research work are as follows : First, for the lack of the initial pheromone of ant colony algorithm and easy premature , an immune clone - ACO . First, using the immune clonal algorithm to generate the initial pheromone distribution , and then integrate into the the immune clone operator of ant colony algorithm search . This method can effectively suppress the stagnation in the convergence process , to improve the search capabilities of the algorithm . Solving large-scale optimization problems , in order to overcome the blindness of roulette , this article introduces a selection rule based on the path of contrast enhancement , the convergence rate of the algorithm has been greatly improved . Improved algorithm is applied to the TSP , the simulation results show that the improved algorithm has better performance than the original algorithm . Second, learn the ant colony algorithm (ACA) and antibody immune clone algorithm (AICA) advantages , a new hybrid algorithm for solving 0-1 knapsack problem . Positive feedback mechanism of the ant colony algorithm , it has a prominent local search performance , but it is easy to fall into stagnation ; while the antibody clone algorithm search area is relatively large , but the convergence is slow . The proposed algorithm takes full advantage of the search capabilities of the former and the latter population diversity . Experimental results show that the algorithm is a convergence speed and optimization capabilities are better optimized . Finally, a summary of the full text of the research work , and look forward to the ant colony optimization issue needs to be studied further .

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CLC: > Mathematical sciences and chemical > Mathematics > Operations Research > Optimization of the mathematical theory
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