基于IWOA算法求解并联冷机负荷分配问题

中央空调系统并联冷水机组系统能耗非常大，如果操作不当，能耗会大大增加。针对OCL问题提出了一种求解连续非线性优化问题的改进鲸群优化算法，首先，为使后续迭代寻优的搜索空间更精确，运用混沌映射初始化种群，使初始解均匀遍布解空间。其次引入变异指数对收敛因子进行改进，平衡了局部勘探和全局勘探的关系。之后引入正弦和余弦使算法收敛到全局最优解，防止了算法过早收敛，提高了算法的收敛精度。最后，通过两个典型的案例来评估IWOA算法的性能，并将其与应用于OCL问题的其他优化算法进行了比较。结果表明，IWOA算法是解决OCL问题的有效方法。此外，算法性能的比较显示，IWOA算法在收敛速度和电能消耗方面相比于其他应用于OCL问题的优化方法提供了更好的解决方案。

IWOA algorithm was used to solve the load distribution problem of parallel chillers

Central air-conditioning system parallel chiller system energy consumption is very large, if improper operation,energy consumption will be greatly increased.An improved whale population optimization algorithm is proposed to solve the continuous nonlinear optimization problem of OCL problem.Firstly, in order to make the search space of the subsequent iteration more accurate, the chaotic mapping is used to initialize the population,so that the initial solutions evenly spread across the solution space. Secondly, the variation index is introduced to improve the convergence factor and balance the relationship between local exploration and global exploration.After that, sine and cosine are introduced to make the algorithm converge to the global optimal solution,which prevents premature convergence of the algorithm and improves the convergence accuracy of the algorithm.Finally, the performance of IWOA algorithm is evaluated by two typical cases and compared with other optimization algorithms applied to OCL problems.The results show that IWOA algorithm is an effective method to solve OCL problem.In addition, the comparison of algorithm performance shows that IWOA algorithm provides a better solution than other optimization methods applied to OCL problems in terms of convergence speed and power consumption.