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中文摘要：
针对粒子群优化（PSO）算法在解决高维非线性优化类问题时存在易陷入局部最小难以寻求最优解的问题，提出了一种具有局部搜索的参数自适应调整的粒子群算法．其核心思想是利用种群分布信息动态调整算法参数；加入混沌变异机制，增加种群多样性；在算法中加入局部搜索机制加强算法局部搜索能力．对6个基准函数的优化结果表明，改进算法具有较好的优化性能．将其用于优化实际的给水管网案例一汉诺塔管网和纽约管网，并与其它算法的结果进行了对比．实验结果表明该算法具有较好的搜索精度和更快的收敛速度．
英文摘要：
The particle swarm optimization （PSO） method can have difficulty reaching local minima and have diffi- culty optimizing high-dimensional nonlinear problems. In order to address these concerns, we propose an a- daptive particle swarm optimization algorithm with local search. The core premise of the algorithm is to adjust the algorithm parameters dynamically based on the population distribution information and to increase popula- tion diversity by incorporating a chaos mutation mechanism. A mechanism is added to strengthen the local search ability of the algorithm. The optimization results of six benchmarking functions show that the algorithm exhibits better optimization performance. We also apply the algorithm to the optimization of two actual network cases： the Hanoi network and the New York network. The results show that the algorithm provides a better search precision and faster convergence speed than other algorithms.
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