Research Of Multi-Objective Optimization Based On Quantum Ant Colony

Multi-objective optimization is widely applied in engineering, industrial andscientific fields, such as: electronic engineering, hydraulic engineering, design andmanufacturing, computer science, robotics and control. Multi-objective optimizationneeds to optimize multiple objectives simultaneously. There are some constraintsamong multiple targets and sometimes a number of objective constraints. As thecomplexity of the problem itself, relevant technology of multi-objective optimizationis still not perfect and mature, there are still many issues to be studied, such as:convergence rate, local optimization, parameter control and how to balance multipleobjectives. How to achieve a multi-objective optimization quickly and effectively isone of hot spots in engineering application.Commonly used multi-objective optimization methods and its own shortcomingsexist in the practical application of many difficulties, has been hampered bymulti-objective optimization method for forward. Research results show that the antcolony optimization problem in most multi-objective evolutionary algorithm than thetraditional better the performance, but solutions of complex multi-dimensionalproblems of the strong, avoiding local optimum, resulting Sauna’s premature. In orderfor ant colony algorithm can effectively overcome these shortcomings, the better tosolve the practical optimization problems. Firstly, the concept of ant colonyoptimization algorithm, background, and future trends of algorithm model based onthe introduction of the multi-objective optimization methods currently used on theexisting multi-objective ant colony optimization models and methods for detailedanalysis, for multi-target ants swarm optimization for solving multi-objectiveoptimization problem shortcomings; then this will be the introduction of ant colonyoptimization theory of quantum computing, quantum derivative method is proposedbased on multi-objective ant colony algorithm. Using quantum genetic algorithm togenerate the initial pheromone distribution, post positive feedback seeking the antcolony algorithm for exact solutions, and strive to complement each other. Algorithmincreases the probability of two qubits as ant current location information, the samenumber in the ant, so that the search space doubled. Can better solve the problem ofant colony algorithm for solving the slow convergence and easy to fall into localoptimum. Finally, the algorithm is used for solving multi-dimensional0-1knapsackproblem. The algorithm and the classical algorithm NSGA2MOA and compare testresults of each method were compared, simulation results show that: the proposedalgorithm can not only faster and more accurate approximation of Pareto optimal front,while able to maintain uniform distribution of Pareto optimal solutions sex. Quantumcomputing algorithm is combined with the ant colony algorithm for a newoptimization method. As the quantum algorithm into a number of basic features ofquantum mechanics, which greatly improves the computational efficiency and searchefficiency and can make up for lack of the ant colony algorithm has broad prospectsfor the study.