Wang Zhengchu

HSAQEA based reliability redundancy optimization for complex system

Complex system reliability function is nonlinear. It is difficult to design a system which is satisfied reliability condition and also has minimum cost. Many intelligent optimization algorithms are used to solve the problem. Many shortcomings still exist such as being trapped into the local optimal solution easily, low convergence efficiency, etc. In this paper, it presents a hybrid simulated annealing quantum evolutionary algorithms (HSAQEA). Adaptive simulated annealing algorithm is embedded in the quantum evolutionary algorithm, and it retains elitist in the evolution in order to accelerate search for efficiency and speed. The presented algorithm is described in detail. The solving strategy is proposed for the problem of complex system reliability optimization. It also analyzes the reliability distribution of bridge connection system. At last, by calculations of the example and comparison with other algorithms, it proves the algorithm has much stronger ability of local search and better search efficiency. It certifies that this method is feasible and valid.

A quantum particle swarm optimization for solving the capacitated vehicle routing problem

The problem of capacitated vehicle routing optimization is studied. Firstly, the mathematic model of VRP and PSO is given. An improved quantum particle swarm optimization is presented. 2-opt and 1-1 opt is used to optimize the inner and outer route. The population entropy is introduced to check whether the algorithm is trapped into local optimization or not, and cataclysm is adopted to ensure the diversities of the solution spaces. The detailed solving steps of VRP based on QPSO are given. Experiment simulation is tested, which shows that QPSO has better performance in searching efficiency and premature problem compared to the other algorithms.

Research in reservoir optimal operation based on modified ant colony optimization

In this paper, the problem of single reservoir operation optimization is studied. Firstly, the background and mathematic model of single reservoir operation optimization are given. Then modified ant colony optimization (MACO) is presented. According to the ergodicity, stochastic property and regularity of chaos, search and optimization are carried out using the chaos variables. Population entropy is introduced to judge whether the algorithm falls in local peak or not, and catastrophe operation is also adopted. Then detailed solving steps of reservoir operation optimization based on MACO are given. Lastly, an instance is given. By calculations of the instance and comparison with other algorithms, it proves the algorithm has much stronger ability of local search and better search efficiency. It also can find better solution and certifies that this method is feasible and valid.