The Improvement Of The Quantum Genetic Algorithm And Its Application Of The Cargo-Loading Problem

Quantum genetic algorithm is a highly efficient intelligent optimization algorithm, combined by quantum algorithm and genetic algorithm. Besides the advantages of genetic algorithm, it also has the abilities of a small population size, fast convergence and global optimization etc. For solving complex optimization problems, Quantum genetic algorithm is an effective solution. But in the complex function optimization, quantum genetic algorithm exists the shortages of slow convergence speed, more iterative times and more easily trapped in local optimal solution. Therefore, make improvement on the traditional quantum genetic algorithm in this paper. The main research work is as follows:First, Put forward an improved quantum genetic algorithm (IQGA):Adopt a dynamic strategy to adjust quantum rotation angle, to speed up the convergence rate. Embed mutation operator to quantum rotation policy dynamically, in order to increase the diversity of population; And through the cataclysm operator make the algorithm to jump out of local optimal point in time, avoid premature convergence.Second, on the basis of IQGA proposed an improved quantum genetic algorithm based on multiple populations (MPIQGA), which replace single population with multiple species. In the process of population initialization use niche strategy evenly to divide the qubit space, which makes each subpopulation evenly distributed to the solution space, and keep the diversity of population. Communication among various groups is through the global optimal individual to update the evolution target. Multiple populations of parallel search can accelerate the search speed, and reduce the number of iterations. Firstly experiment is through a number of complex continuous functions to verify the feasibility and effectiveness of the improved quantum genetic algorithms.The cargo-loading problem of logistics distribution belongs to the constrained optimization problem in the field of engineering. In this paper, using the IQGA and MPIQGA to solve a model of a multi-model and multi-cargo loading problem. Convert the constraints of the model into a penalty function and add to the fitness function, and join the vehicle merging idea to reduce the number of vehicles. The experimental results show that the new algorithms for cargo loading problem is feasible and effective, and the new algorithm has certain application value.