Research On Ant Colony Optimization Algorithm And Model Of Ant Colony Foraging Behavior

The ant colony optimization (ACO) is a heuristic method which is proposed bysimulating the mechanism that the ant colony can find out the shortest path betweenthe nest to a food source. The algorithm is being the important method to solve thecomplex optimization problem as soon as being proposed. Nowadays, researches onACO mainly include two fields: one is ACO based on probability function insolution space, which solves optimization problem by the determination of statetransition probability and regeneration pattern of pheromone; the other is the modelbased on basic ant’s behavior rule, which reveals the characteristics, results andcomplexity causes of the ant colony’ behavior, such as the foraging and clusteringetc, through the behavior evolution based on the rule. In this thesis, the two researchfields of ACO are deeply studied respectively. Firstly, by the use of the complexadaptive systems theory, the ant colony foraging behavior model based on Agent isestablished. Next, the analysis and improvement of ACO based on probabilityfunction in solution space is studied. Finally, the model and the algorithms are usedin actual engineering problem, and the simulation results verify the effective and th epracticality of the methods.Main research contents are as follows:（1）According to the principle of the ant foraging behavior, an ant colonyforaging behavior model based on Agent is proposed by the use of the complexadaptive systems theory. Through simulating and analyzing several importantparameters in the model, the models based on adaptive parameters and added newbehavior rules are proposed respectively. And with the addition of the new behaviorrules and the new parameters, the better effective is obtained in the simulation offinding food source;（2）To overcome the premature and stagnation phenomenon in basic ACOalgorithm, this paper proposes an improved ACO algorithm which emphasizes dataprocessing and bases on symmetry degree city selection and pheromoneregeneration, and proves the convergence of this algorithm. Finally, experimentalresults show that this algorithm can overcome the defects of premature andstagnation, and accelerate convergence;（3）Due to the lack of ACO in solving optimization problem in continuespace, on this basis of extended ACO proposed by M. Dorigo, who is founder ofACO algorithm, this paper proposes an extended ACO based on uniform parameterselection and weighted improvement of solution by studying implication ofalgorithm parameter and convergence of extended ACO algorithm. Simulation experiments show that this algorithm has feasibility and efficiency in solvingoptimization problem in continue space;（4）Aiming to the shortcomings of the extended ACO, this paper proposesthree hybrid algorithms of ACO. Firstly, an improved quantum extended ACO isproposed to solve continue optimization problem. This algorithm codes individualby using probability amplitude of quantum bit and fulfills mutation by quantum notgate. Secondly, this paper proposed a genetic extended ACO based on cloudy model.This algorithm obtains initial solution of extended ACO by GA and uses the cloudymodel to adjust the two parameters in extended ACO adaptively. Thirdly, this paperproposes an extended ACO based artificial fish swarm algorithm. This algorithmobtains initial solution of extended ACO by artificial fish-swarm algorithm andadds random foraging behavior of fish swarm in each iteration. Many multi-dimensions continue function simulation experiments show the advantages of thethree extended algorithms to solve optimization problem in continue space;（5）As fuzzy rules and control parameters in fuzzy neural controller aredifficult to acquired, this paper puts forward two fuzzy neural network controllersbased on extended quantum ACO. One is the normal fuzzy neural networkcontroller of which the parameter is optimized by extended quantum ACO. On thebasis of the controller, through designing the variable universe contractionexpansion factor and membership function, the variable universe fuzzy neuralcontroller is proposed. Finally, using these two controller to the single levelinverted pendulum system respectively, and compare with other controllers,simulation results show that this controller has better control performance;（6）According to the similarity between the process of ant colony foragingbehavior and the robot path planning, the improved ant colony foraging behaviormodel and modified discrete domain ant colony algorithm are used to the robot pathplanning in the static and dynamic complex environment, the experiment resultsverify the adaptability and the effectiveness of the methods.