Hybrid Quantum-Inspired Evolutionary Algorithm And Its Application In The Substations Planing

The quantum-inspired evolutionary algorithm (QIEA) is a kind of probability search method, which is combined the quantum computing and evolutionary algorithm. It is a rising star in the evolutionary algorithm family. Compared with traditional evolutionary algorithm, the quantum-inspired evolutionary algorithm are more likely to achieve a balance between exploitation and exploration. It has advantages of small population size, fast convergence speed and strong global search capability. However, the QIEA is easy to rapid convergence and result in precocious phenomena when it solves some complex optimization problems. So it can’t get the optimal solution. So the QIEA still need to further improve the search ability. There are many improvement strategies about the research of the QIEA. One of the QIEA’s important research directions is by combining with other optimization algorithm and then it can form a new evolution search algorithm. Because all kinds of optimization algorithm have its own advantages and disadvantages, so different optimization algorithm can largely enhance advantages and make up for disadvantages by reasonable combination. This paper combines with the quantum evolutionary algorithm (QIEA) and greed random adaptive search algorithm (GRASP) and puts forward a new hybrid quantum evolutionary algorithm (HQIEA). The GRASP is good at the local search. The HQIEA has the advantage both QIEA and GRASP and abandons theirs shortage. The HQIEA is applied to the urban distribution network planning optimization.Substation planning is the most important part urban distribution network planning and substation planning results directly affect the investment of the power network、power distribution reliability and operational efficiency. The paper applies the HQIEA to the110kV substations planning work of an area. The experimental results show that method is very good to complete the capacity and the site of the substations in the region. The results comply with current power demand and substations construction trend of the county, it has important reference value to the current power grid planning construction planning.The main work and research results are as follows:1. The basic theory and related concepts of QIEA are introduced. The problems need to be solved are also pointed out. The algorithm process of QIEA also has been described. Genetic algorithm (GA) is one of the traditional evolutionary algorithms and as the comparative method in the knapsack problem experiments. By the comparative analysis, the experimental results show that QIEA has very good ability to exploration and strong global search ability. The QIEA has better performance in solving combinatorial optimization problems that compared with the GA. It has very important research value.2. To improve the search performance and enhance the exploitation ability of QIEA. This paper discusses five kinds of typical local methods in current literature. Their performance is compared with each other, the advantages and disadvantages are summarized. GRASP is elected to best performance of them, and puts forward a new hybrid quantum evolutionary algorithm (HQIEA). The HQIEA has better performance in solving knapsack problems that compared with several methods in current literature. It has the convergence speed, ideal global search ability and local search ability. The HQIEA has superior performance in solving combinatorial optimization problems.3. The HQIEA is applied to the substation planning optimization. The method of the current literature is as the comparative method in the110kV substations planning experiments of one area. The experimental results show that the HQIEA can well solve the substation planning optimization work. The optimization scheme is feasible and it gets the optimal object of investment in the least, running in the most economic, reliability of power supply. The results comply with current power demand and substations construction trend of the county, it has important reference value to the current power grid planning construction planning. It shows the effectiveness of the method. So as to the application range of the QIEA and its improved algorithm is expanded.This paper work is supported by the program for New Century Excellent Talents in university (NCET-11-0715), the project sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry and the National Natural Science Foundation of China (61170016).