Abstract:

To improve the performance of Yin-Yang-Pair Optimization-Simulated Annealing1 （YYPO-SA1）， a Yin-Yang-pair optimization algorithm based on dynamic D-way splitting and chaotic perturbation NYYPO （Newton-Yin-Yang-Pair Optimization） was proposed. Firstly， in order to dynamically adjust the probability of D-way splitting， Newton’s law of cooling mechanism was adopted. Then， the chaotic perturbation strategy was applied in splitting stage. The dynamic adjustment mechanism was applied to enable NYYPO to use a larger D-way segmentation probability at the early stage of search， and use a smaller D-way segmentation probability at the late stage of search， which enhanced the global search ability of the algorithm. Meanwhile， the diversity of solution was enriched， and the ability of the algorithm to jump out of local optimum was improved by using chaotic perturbation strategy. Finally， NYYPO was applied to the parameter optimization design problem of wind-driven generator. Fifteen test functions， including unimodal， multimodal， and composite functions， were selected to evaluate the performance of NYYPO， YYPO-SA1， and 6 representative single-objective optimization algorithms： Particle Swarm Optimization （PSO） algorithm， Crow Search Algorithm （CSA）， Grey Wolf Optimizer （GWO）， Whale Optimization Algorithm （WOA）， Flower Pollination Algorithm （FPA）， and Sparrow Search Algorithm （SSA）. The results show that compared with YYPO-SA1， NYYPO obtains 12 orders of magnitude improvement on Sphere function. In Friedman test， when dimension is 10， 30， 50 respectively， NYYPO ranks 2.87， 2.0 and 1.93 averagely and respectively， total ranking of all of them is the first. It can be seen that NYYPO achieves significant performance advantages in statistical significance. At the same time， NYYPO also achieves better optimization results in the parameter optimization design problem of wind-driven generator.

Key words: Yin-Yang-Pair Optimization (YYPO) algorithm, probability of D-way splitting, Newton’s law of cooling, disturbance strategy, tent chaos

摘要：

为提高YYPO-SA1的性能，提出了一种基于动态D向分割和混沌扰动的阴阳对优化算法（NYYPO）。首先，基于牛顿衰减机制来动态调整YYPO-SA1中的D向分割概率；然后，在分割阶段加入混沌扰动策略，NYYPO利用动态调整机制在搜索前期使用较大的D向分割概率，在搜索后期则使用较小的D向分割概率，从而提高了算法的全局搜索能力，同时使用混沌扰动策略丰富了解的多样性，并提高了算法跳出局部最优的能力；最后，将NYYPO应用于风力发电机的参数优化设计问题。选用了15个单峰、多峰和组合测试函数进行性能评估，将NYYPO、YYPO-SA1以及6个代表性的单目标优化算法：粒子群优化（PSO）算法、乌鸦搜索算法（CSA）、灰狼优化算法（GWO）、鲸鱼优化算法（WOA）、花授粉算法（FPA）、麻雀搜索算法（SSA）进行性能评测比较。结果表明NYYPO相较于YYPO-SA1在Sphere函数上有着12个数量级的提升。而在Friedman检验中NYYPO在10维、30维、50维的时候的平均排名分别为2.87、2.0、1.93，均为总排名第一，可见NYYPO在统计学意义上具有显著的性能优势。同时，在风力发电机参数优化设计问题中NYYPO也取得了更好的优化结果。

关键词: 阴阳对优化算法, D向分割概率, 牛顿衰减, 扰动策略, tent混沌