Elite Opposition-Based Learning Golden-Sine Harris Hawks Optimization

doi:10.3778/j.issn.1002-8331.2011-0321

Abstract

Abstract: The basic Harris hawks optimization（HHO） is easy to fall into local optimal value and has low convergence precision as well as slow convergence speed. An elite opposition-based learning golden-sine Harris hawks optimization（EGHHO） is proposed. Elite opposition-based learning mechanism is used to improve the diversity and quality of the population, improve the global optimization capability and convergence precision. Golden-sine algorithm is introduced to improve the way that Harris hawk hunts prey, effectively reducing the search space, reducing the convergence time and enhancing the ability of local mining. By solving multiple unimodal, multi-modal and high-dimensional test functions, the results show that the EGHHO algorithm, which combines the two strategies, has a stronger ability to jump out of local extremes, as well as higher optimization precision and speed.

Key words: Harris hawks optimization algorithm, elite opposition-based learning, golden-sine algorithm, high-dimensional optimization

摘要： 针对基本哈里斯鹰优化算法（Harris hawks optimization，HHO）易陷入局部最优值、收敛精度低和收敛速度慢的问题，提出融合精英反向学习与黄金正弦算法的哈里斯鹰优化算法（elite opposition-based learning golden-sine Harris hawks optimization，EGHHO）。融入精英反向学习机制，提高种群多样性和种群质量，提升算法全局寻优性能和收敛精度；融入黄金正弦算法优化哈里斯鹰围捕猎物的方式，有效缩小搜索空间，减少算法收敛时间，增强算法局部开发能力。通过求解多个单模态、多模态和高维度测试函数进行算法之间的对比，结果表明，融合两种策略的EGHHO算法具有较强跳出局部极值的能力以及更高的寻优精度和寻优速度。

关键词: 哈里斯鹰优化算法, 精英反向学习, 黄金正弦算法, 高维优化

GUO Yuxin, LIU Sheng, GAO Wenxin, ZHANG Lei. Elite Opposition-Based Learning Golden-Sine Harris Hawks Optimization[J]. Computer Engineering and Applications, 2022, 58(10): 153-161.

郭雨鑫, 刘升, 高文欣, 张磊. 精英反向学习与黄金正弦优化的HHO算法[J]. 计算机工程与应用, 2022, 58(10): 153-161.

References

[1] HEIDARI A A，SEYEDALI M，HOSSAM F，et al.Harris hawks optimization：algorithm and applications[J].Future Generation Computer Systems，2019，97（12）：849-872.
[2] CHEN H L，JIAO S，WANG M J，et al.Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts[J].Energy Conversion and Management，2019，195（24）：927-942.
[3] ANEESH W，MANOJ K N，RUTUPARNA P，et al.An adaptive Harris hawks optimization technique for two dimensional grey gradient based multilevel image thresholding[J].Applied Soft Computing，2020，95：106526.
[4] DU P，WANG J Z，HAO Y，et al.A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM 2.5 and PM 10 forecasting[J].Applied Soft Computing Journal，2020，96：106620.
[5] 赵世杰，高雷阜，于冬梅，等.融合能量周期性递减与牛顿局部增强的改进HHO算法[J].控制与决策，2021，36（3）：629-636.
ZHAO S J，GAO L F，YU D M，et al.Improved Harris hawks optimization coupling energy cycle decline mechanism and Newton local enhancement strategy[J].Control and Decision，2021，36（3）：629-636.
[6] KAMBOJ V K，NANDI A，BHADORIA A，et al.An intensify Harris hawks optimizer for numerical and engineering optimization problems[J].Applied Soft Computing，2020，89：106018.
[7] AL-BETAR M A，AWADALLAH M A，HEIDARI A A，et al.Survival exploration strategies for Harris hawks optimizer[J].Expert Systems with Applications，2021，168：114243.
[8] MEHRDAD G，ZAHRA Z，YAZDAN A.Computer simulation study of the Levy flight process[J].Physicia A：Statistical Mechanics and its Applications，2009，388（8）：1509-1514.
[9] TIZHOOSH H R.Opposition-based learning：a new scheme for machine intelligence[C]//International Conference on Computational Intelligence for Modelling，Control and Automation，2005 International Conference on Intelligent Agents，Web Technologies and Internet Commerce，Vienna，2005：695-701.
[10] SAUNHITA S，MINI S.Opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling for global optimization[J].Soft Computing，2019，23（15）：6023-6041.
[11] TUBISHAT M，IDRIS N，SHUIB L，et al.Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection[J].Expert Systems with Applications，2020，145（C）：113122.
[12] SEIF Z，AHMADI M B.An opposition-based algorithm for function optimization[J].Engineering Applications of Artificial Intelligence，2015，37（10）：293-306.
[13] 肖子雅，刘升.精英反向黄金正弦鲸鱼算法及其工程优化研究[J].电子学报，2019，47（10）：2177-2186.
XIAO Z Y，LIU S.Study on elite opposition-based golden-sine whale optimization algorithm and its application of project optimization[J].Acta Electronica Sinica，2019，47（10）：2177-2186.
[14] 方旭阳，武相军，游大涛.具有学习机制的正弦余弦算法[J].计算机应用研究，2020，37（3）：809-813.
FANG X Y，WU X J，YOU D T.Sin-cosine algorithm with learning mechanism[J].Application Research of Computers，2020，37（3）：809-813.
[15] TANYILDIZI E，DEMIR G.Golden sine algorithm：a novel math-inspired algorithm[J].Advances in Electrical & Computer Engineering，2017，17（2）：71-78.
[16] ARORA S，SINGH S.Butterfly optimization algorithm：a novel approach for global optimization[J].Soft Computing，2019，23（3）：715-734.
[17] KHISHE M，MOSAVI M.Chimp optimization algorithm[J].Expert Systems with Applications，2020，149（24）：113338.
[18] 龙文，蔡绍洪，焦建军，等.求解大规模优化问题的改进鲸鱼优化算法[J].系统工程理论与实践，2017，37（11）：2983-2994.
LONG W，CAI S H，JIAO J J，et al.Improved whale optimization algorithm for large scale optimization problems[J].Systems Engineering—Theory & Practice，2017，37（11）：2983-2994.