基于捕食搜索策略的模拟退火优化算法

针对传统模拟退火算法初始温度和降温函数难以确定以及接收劣质解同时容易遗失当前最优解等缺陷，将禁忌搜索算法的禁忌表功能引入SA算法，避免遗失最优解和对某个解进行多次重复地搜索；根据函数的复杂程度确定初始温度，并定义新的降温函数，提高算法的搜索效率和精度；引入捕食搜索策略，平衡算法搜索能力和开发能力，避免陷入局部最优。通过对5个典型的基准测试函数的仿真表明，改进算法具有较强的全局搜索能力，同时寻优精度和收敛速度比原算法也有较大的提高。

A simulated annealing algorithm based on predatory search strategy

The traditional Simulated Annealing Algorithm is difficult to determine the initial temperature and the cooling function as well as easy to receive the inferior solution at the same time losing the current optimal solution. In order to improve the global search capability and the computational efficiency of the Simulated Annealing, this paper consider to take the tabu table of Tabu Search Algorithm into the Simulated Annealing to avoid the loss of the optimal solution and searching repeatedly for a solution. At the same time by determining the initial temperature according to the complexity of the function and defining the new cooling function to improve the efficiency and accuracy of the algorithm. Then introducing predator search strategy to balance the search ability and development ability to avoid getting trapped into local optima. The results on five typical standard test functions show that the improved Simulated Annealing Algorithm not only improves the global search ability, but also the search efficiency, search accuracy and convergence rate are better than the traditional Simulated Annealing Algorithm.