基于趋化校正的哈里斯鹰优化算法

针对哈里斯鹰优化（HHO）算法收敛速度慢、易陷入局部最优的缺点，提出了一种改进HHO算法，即基于趋化校正（CC）的哈里斯鹰优化（CC-HHO）算法。首先，通过计算最优解下降率和变化权重来识别收敛曲线的状态；其次，将细菌觅食优化（BFO）算法的CC机制引入局部搜索阶段来提高寻优的精确性；再次，将生物在运动时的能量消耗规律融入逃逸能量因子和跳跃距离的更新过程中，从而更好地平衡算法的探索与开发；然后，对最优解和次优解的不同组合进行精英选择来拓展算法全局搜索的广泛性；最后，当搜索陷入局部最优时，通过对逃逸能量施加扰动来实现强制跳出。通过10个基准函数对改进算法的性能进行测试，结果显示CC-HHO算法对单峰函数的搜索精度比引力搜索算法（GSA）、粒子群优化（PSO）算法、鲸优化算法（WOA）以及另外4种改进的HHO算法提升超过10个数量级；对多峰函数也有超过1个数量级的优势；在保证搜索稳定性平均提升超过10%的前提下，所提算法的收敛速度明显优于上述几种优化算法，收敛趋势更加明显。实验结果表明，CC-HHO算法有效地提高了原算法的搜索效率和鲁棒性。

Harris hawks optimization algorithm based on chemotaxis correction

Focused on the disadvantages of slow convergence and easy to fall into local optimum of Harris Hawks Optimization （HHO） algorithm， an improved HHO algorithm called Chemotaxis Correction HHO （CC-HHO） algorithm was proposed. Firstly， the state of convergence curve was identified by calculating the rate of decline and change weight of the optimal solution. Secondly， the CC mechanism of the Bacterial Foraging Optimization （BFO） algorithm was introduced into the local search stage to improve the accuracy of optimization. Thirdly， the law of energy consumption was integrated into the updating process of the escape energy factor and the jump distance to balance the exploration and exploitation. Fourthly， elite selection for different combinations of optimal solution and sub-optimal solution was used to improve the universality of global search of the algorithm. Finally， when the search was falling into local optimum， the escape energy was disturbed to realize the forced jumping out. The performance of the improved algorithm was tested by ten benchmark functions. The results show that the search accuracy of CC-HHO algorithm on unimodal functions is better than those of Gravitational Search Algorithm （GSA）， Particle Swarm Optimization （PSO） algorithm， Whale Optimization Algorithm （WOA） and other four improved HHO algorithms for more than ten orders of magnitude； there is also more than one order of magnitude superiority on multimodal functions； on the premise that search stability is improved by more than 10% on average， the proposed algorithm has faster convergence speed significantly than the above-mentioned several comparative optimization algorithms with more obvious convergence trend. Experimental results show that CC-HHO algorithm effectively improves the efficiency and robustness of the original algorithm.