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In this paper, proposes an improved quantum genetic algorithm based on RCPSP. Project implementation process often involves the sharing of resources and conflicts between the different projects, so in the case of how resource-constrained project scheduling resources to meet the requirements of different projects and shorten the duration of each project is an important issue. Algorithm is based on the priority activities quantum encoding, combined with adjacency matrix storage activities, effectively solved the phenomenon of illegal activity scheduling, using preemptive mode of resource allocation to schedule resources, thus avoiding resource allocation conflicts problem. Experiments show that, through multiple iterations of the computer's parallel computing can be effectively come to meet the multi-project resource constrained scheduling. Introduction The first quantum algorithm to solve the decomposition of large number of factors and put forward in 1996 quantum algorithms for random database search have attracted much attention due to their unique computational performance, and have rapidly become a hotspot in the world. Quantum genetic algorithm is a probability optimization algorithm based on quantum computing principle. It has the advantages of small population size, fast convergence speed and strong global search ability. It is mainly applied to combinatorial optimization, such as Traveling Salesman problem, Knapsack problem. At present, all kinds of quantum genetic algorithms (such as mixed quantum, parallel quantum) are almost all varieties of the quantum genetic algorithm. According to the literature [1].These algorithms mainly have the following problems: (1) By measuring the state of the quantum bits to obtain binary solution, which is a probabilistic operation process, with great randomness and blindness, in the process evolution of the population, some individuals will inevitably produce degradation. (2) For the rotation angle of the quantum revolving door, it is almost based on the query table or its variants, which affects the efficiency of the algorithm because of the multi-path condition judgment. For the size of the rotation angle, the existing method treats all the population equally, without considering the differences between the chromosomes. Based on the problems above, this paper presents an improved quantum genetic algorithm.

An improved quantum genetic algorithm for the Resource Constrained Project Scheduling