差分扰动的堆优化算法

针对堆优化算法（HBO）在解决复杂问题时存在搜索能力不足和搜索效率低等缺陷，提出一种差分扰动的HBO——DDHBO。首先，提出一种随机差分扰动策略更新最优个体的位置，以解决HBO没有对其更新从而导致的搜索效率低的问题；其次，使用一种最优最差差分扰动策略更新最差个体的位置，以强化其搜索能力；然后，采用一种多层差分扰动策略更新一般个体的位置，以强化多层个体之间的信息交流，并提高搜索能力；最后，针对原更新模型在搜索初期获得有效解概率低的问题，提出一种基于维的差分扰动策略更新其他个体的位置。在大量CEC2017复杂函数上的实验结果表明，与HBO相比，DDHBO在96.67%的函数上具有更好的优化性能，更少的平均运行时间（3.445 0 s）；与WRBBO（Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization）、DEBBO（Differential Evolution and Biogeography-Based Optimization）和HGWOP（Hybrid PSO and Grey Wolf Optimizer）等先进算法相比，DDHBO也具有显著的优势。

Differential disturbed heap-based optimizer

In order to solve the problems， such as insufficient search ability and low search efficiency of Heap-Based optimizer （HBO） in solving complex problems， a Differential disturbed HBO （DDHBO） was proposed. Firstly， a random differential disturbance strategy was proposed to update the best individual’s position to solve the problem of low search efficiency caused by not updating of this individual by HBO. Secondly， a best worst differential disturbance strategy was used to update the worst individual’s position and strengthen its search ability. Thirdly， the ordinary individual’s position was updated by a multi-level differential disturbance strategy to strengthen information communication among individuals between multiple levels and improve the search ability. Finally， a dimension-based differential disturbance strategy was proposed for other individuals to improve the probability of obtaining effective solutions in initial stage of original updating model. Experimental results on a large number of complex functions from CEC2017 show that compared with HBO， DDHBO has better optimization performance on 96.67% functions and less average running time （3.445 0 s）， and compared with other state-of-the-art algorithms， such as Worst opposition learning and Random-scaled differential mutation Biogeography-Based Optimization （WRBBO）， Differential Evolution and Biogeography-Based Optimization （DEBBO）， Hybrid Particle Swarm Optimization and Grey Wolf Optimizer （HGWOP）， etc.， DDHBO also has significant advantages.