融合混沌对立和分组学习的海洋捕食者算法

doi:10.3778/j.issn.1002-8331.2105-0130

摘要/Abstract

摘要： 针对海洋捕食者算法存在收敛速度慢、不易逃出局部最优的缺点，提出了一种改进海洋捕食者算法。将混沌映射与对立学习策略相结合，在保证遍历性和随机性的同时，生成高质量的初始猎物种群。引入自适应t分布变异算子更新种群，增加种群多样性，避免陷入局部最优。对更新后的种群，按照适应度分为精英组和学习组，学习组向精英组猎物的平均维度进行学习，精英组内的猎物相互维度学习，进一步提高种群质量和搜索精度。选取15个测试函数，通过对比测试，验证了改进后的算法可以有效提高原算法的收敛速度和寻优精度。将改进后的算法应用于无线传感器网络覆盖优化，实验结果显示，改进后的算法提高了网络覆盖率，优化后的节点分布更加均匀。

关键词: 混沌映射, 对立学习, 自适应t分布, 分组学习, 海洋捕食者算法

Abstract: For the shortcomings of the marine predator algorithm, such as slow convergence speed and difficult to escape from the local optimum, an improved marine predator algorithm is proposed. Firstly, chaotic mapping is combined with opposition learning strategy to generate high-quality initial prey population while ensuring ergodicity and randomness. Secondly, the adaptive t-distribution mutation operator is introduced to update the population to increase the diversity of the population and avoid falling into the local optimum. For the updated population, it is divided into elite group and learning group according to fitness. The learning group learns from the average dimension of prey in the elite group, and the prey in the elite group learns from each other to further improve the population quality and search accuracy. 15 test functions are selected and compared to verify that the improved algorithm can effectively improve the convergence speed and optimization accuracy of the original algorithm. Finally, the improved algorithm is applied to wireless sensor network coverage optimization, the experimental results show that the improved algorithm improves the network coverage, and the optimized node distribution is more uniform.

Key words: chaotic mapping, opposition-based learning, adaptive t-distribution, group learning, marine predator algorithm

马驰, 曾国辉, 黄勃, 刘瑾. 融合混沌对立和分组学习的海洋捕食者算法[J]. 计算机工程与应用, 2022, 58(22): 271-283.

MA Chi, ZENG Guohui, HUANG Bo, LIU Jin. Marine Predator Algorithm Based on Chaotic Opposition Learning and Group Learning[J]. Computer Engineering and Applications, 2022, 58(22): 271-283.

参考文献

[1] WU G H，Across neighborhood search for numerical opti-mization[J].Information Sciences，2016，329：597-618.
[2] 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.
[3] 彭鹏，倪志伟，朱旭辉，等.基于改进二元萤火虫群优化算法和邻域粗糙集的属性约简方法[J].模式识别与人工智能，2020，33（2）：95-105.
PENG P，NI Z W，ZHU X H，et al.Attribute reduction method based on improved binary glowworm swarm optimization algorithm and neighborhood rough set[J].Pattern Recognition and Artificial Intelligence，2020，33（2）：95-105.
[4] 张德惠，游晓明，刘升.融合猫群算法的动态分组蚁群算法[J].计算机科学与探索，2020，14（5）：880-891.
ZHANG D H，YOU X M，LIU S.Dynamic grouping ant colony algorithm combined with cat swarm optimization[J].Journal of Frontiers of Computer Science and Technology，2020，14（5）：880-891.
[5] ZHANG L，LIU M，HAO J，et al.Scheduling semiconductor wafer fabrication using a new harmony search algorithmbased on receipt priority interval[J].Chinese Journal of Electronics，2016，25（5）：866-872.
[6] FARAMARZI A，HEIDARINEJAD M，MIRJALILI S，et al.Marine predators algorithm：a nature-inspired metaheuristic[J].Expert Systems with Applications，2020，152：113377.
[7] ELAZIZ M A，EWEES A A，YOUSRI D，et al.An improved marine predators algorithm with fuzzy entropy for multi-level thresholding：real world example of COVID-19 CT image segmentation[J].IEEE Access，2020，8：125306-125330.
[8] ABDEL-BASSE M，MOHAMED R，ELHOSENY M，et al.A hybrid COVID-19 detection model using an improved marine predators algorithm and a ranking-based diversity reduction strategy[J].IEEE Access，2020，8：79521-79540.
[9] FAN Q S，HUNAG H S，CHEN Q P，et al.A modified self-adaptive marine predators algorithm：framework and engineering applications[J].Engineering with Computers，2022，38：3269-3294.
[10] ELAZIZ M A，THANIKANTI S B，LBRAHIM I A，et al.Enhanced marine predators algorithm for identifying static and dynamic photovoltaic models parameters[J].Energy Conversion and Management，2021，236：113971.
[11] 邵良杉，李臣浩.基于改进花粉算法的极限学习机分类模型[J].计算机工程与应用，2020，56（1）：172-179.
SHAO L S，LI C H.Classification model of extreme learning machine based on improved pollen algorithm[J].Computer Engineering and Applications，2020，56（1）：172-179.
[12] TIZHOOSH H R.Opposition-based learning：a new scheme for machine intelligence[C]//International Conference on Computational Intelligence for Modelling，Control and Automation and International Conference on Intelligent Agents，Vienna，2005：695-701.
[13] 岳小雪，郑云水，林俊亭.基于改进WNN的城市轨道交通客流量预测[J].计算机工程与应用，2016，52（11）：227-232.
YUE X X，ZHENG Y S，LIN J T.Passenger flow forecast of urban rail transit based on improved WNN[J].Computer Engineering and Applications，2016，52（11）：227-232.
[14] 赵世杰，高雷阜，于冬梅，等.基于变因子加权学习与邻代维度交叉策略的改进CSA算法[J].电子学报，2019，47（1）：40-48.
ZHAO S J，GAO L F，YU D M，et al.Improved CSA algorithm based on variable factor weighted learning and adjacent generation dimension crossover strategy[J].Acta Electronica Sinica，2019，47（1）：40-48.
[15] 郭佳丽，王秋萍，王晓峰.融合学习策略和邻域搜索的飞蛾火焰算法[J].计算机工程与应用，2021，57（12）：170-179.
GUO J L，WANG Q P，WANG X F.Moth flame optimization based on learning strategy and neighborhood search[J].Computer Engineering and Applications，2021，57（12）：170-179.
[16] 张水平，高栋.基于随机替换和混合变异的蜻蜓算法[J].科学技术与工程，2020，20（22）：9108-9115.
ZHANG S P，GAO D.Dragonfly algorithm based on random substitution and hybrid mutation[J].Science Technology and Engineering，2020，20（22）：9108-9115.
[17] 彭智，谢玲.混合优化算法的全局收敛性分析[J].北京理工大学学报，2012，32（4）：435-440.
PENG Z，XIE L.Global convergence analysis of hybrid optimization algorithms[J].Transactions of Beijing Institute of Technology，2012，32（4）：435-440.
[18] DERRAC J，GARCíA S，MOLINA D，et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm & Evolutionary Computation，2011，1（1）：3-18.
[19] 唐菁敏，曲文博，苏慧慧，等.一种基于帝企鹅差分算法的WSN覆盖优化[J].云南大学学报（自然科学版），2021，43（1）：46-51.
TANG J M，QU W B，SU H H，et al.Coverage optimization of WSN based on emperor penguin difference algorithm[J].Journal of Yunnan University（Natural Sciences Edition），2021，43（1）：46-51.