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Improved Ant Colony Algorithm And Its Application

Posted on:2018-04-07Degree:MasterType:Thesis
Country:ChinaCandidate:Y Q ZhangFull Text:PDF
GTID:2348330542972534Subject:Probability theory and mathematical statistics
Abstract/Summary:PDF Full Text Request
Ant colony algorithm has the obvious advantage in solving many complex combinatorial optimization problems.We apply this intelligent optimization algorithm to avoid the unfavorable factors in the life and production.Seeking the maximum benefit can make the life more convenient and production more efficient.The algorithm has important significance in practical application.In this paper,three improved ant colony optimization algorithms for path optimization problem,vehicle scheduling problem and function optimization problem are emphatically studied.The application of Algorithm is also introduced to solve these problems.The main contents are as follows:First,an improved ant colony algorithm is proposed to solve the TSP problem.Because the basic ant colony algorithm is the shortest path in accordance with the principles of neighboring nodes to select the next node,the global path is not necessarily the best choice.Aimed at the disadvantage,the two-node shortest path strategy of selecting the next node methods,improves path selection of ant colony algorithm and adjusts tabu list of nodes in sequence,then the improved algorithm is applied to tourism route optimization and simulation by using the TSPLIB Benchmark31,Att48,kro A100,Pr136,tsp225 problem.Better results which are compared with the basic ant colony algorithm are gained by the improved ant colony algorithm.Second,an improved ant colony algorithm is proposed to solve the problem of vehicle scheduling path optimization.A saving matrix is introduced as a priori information to guide the ant search,and then the different pheromone volatile factors are used to make a balance of the “search” and “utilization”,and the optimal node is adjusted by 2-opt method.The improved algorithm is applied to the vehicle routing problem,and a better path is obtained than the basic ant colony algorithm.Third,an improved ant colony algorithm is proposed to solve the problem of function optimization.The basic ant colony algorithm is easy to fall into the local search and the convergence rate is slow and so on.For solving the shortcomings of the algorithm,the optimal ant colony algorithm is obtained.The optimal position of the ant colony algorithm is adjusted and the pheromone increment of the ant colony algorithm is adjusted by the optimal result of the pollination algorithm.The hybrid algorithm ofimproved ant colony algorithm based on the flower pollination algorithm is proposed.The simulation experiments are made by 6 test function and tested the improved algorithm's effectiveness,then the improved algorithm is applied to portfolio optimization problem and the investment portfolio risk effectively reduces.Finally,summary is made in this paper,and the future research problems are prospected.
Keywords/Search Tags:ant colony algorithm, flower pollination algorithm, TSP, vehicle scheduling, function optimization
PDF Full Text Request
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