改进多元宇宙算法求解大规模实值优化问题
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摘要
针对多元宇宙优化(MVO)算法中虫洞存在机制、白洞选择机制等不足,该文提出一种改进多元宇宙优化算法(IMVO)。设计固定概率的虫洞存在机制和前期快速收敛后期平缓收敛的虫洞旅行距离率,加快算法全局探索能力和快速迭代能力;提出黑洞的随机白洞选择机制,设计黑洞围绕白洞恒星进行公转并模型化,解决代间宇宙信息沟通的问题,中低维度数值比较实验验证了改进算法的优良性能。选取大规模实值问题较难优化的3个基准测试函数进行对比实验,改进算法在大规模优化问题上的求解精度和成功率方面具有较好的适用性和鲁棒性。
To overcome the mechanism shortcomings of wormhole and white hole selection in the Multi-Verse Optimizer(MVO), an Improved Multi-Universes Optimization(IMVO) algorithm is proposed. To speed up global exploration ability and quick iteration ability, this thesis designs the existence mechanism of wormhole with fixed probability and the Travel Distance Rate(TDR) that its convergence from early stage's smoothly to later stage's fast. The random white hole selection mechanism is proposed; Black holes can revolve around selected white hole stars and is modelled to solve the problem of information communication of the Intergenerational Universes. The performance of IMVO is verified by comparison experiments in low-middle dimensions. Three benchmarks test functions are selected for comparison in large scale which are difficult to be optimized, the experimental results show that IMVO has good applicability and robustness with higher solving accuracy and success rate in large scale optimization problem.
To overcome the mechanism shortcomings of wormhole and white hole selection in the Multi-Verse Optimizer(MVO), an Improved Multi-Universes Optimization(IMVO) algorithm is proposed. To speed up global exploration ability and quick iteration ability, this thesis designs the existence mechanism of wormhole with fixed probability and the Travel Distance Rate(TDR) that its convergence from early stage's smoothly to later stage's fast. The random white hole selection mechanism is proposed; Black holes can revolve around selected white hole stars and is modelled to solve the problem of information communication of the Intergenerational Universes. The performance of IMVO is verified by comparison experiments in low-middle dimensions. Three benchmarks test functions are selected for comparison in large scale which are difficult to be optimized, the experimental results show that IMVO has good applicability and robustness with higher solving accuracy and success rate in large scale optimization problem.
引文
[1]MAHDAVI S,RAHNAMAYAN S,and SHIRI M E.Multilevel framework for large-scale global optimization[J].Soft Computing,2017,21(14):4111-4140.doi:10.1007/s00500-016-2060-y.
[2]BOLUFé-R?HLER A,FIOL-GONZáLEZ S,and CHEN S.A minimum population search hybrid for large scale global optimization[C].Proceedings of 2015 IEEE Congress on Evolutionary Computation,Sendai,Japan,2015:1958-1965.doi:10.1109/CEC.2015.7257125.
[3]梁静,刘睿,于坤杰,等.求解大规模问题协同进化动态粒子群优化算法[J].软件学报,2018,29(9):2595-2605.doi:10.13328/j.cnki.jos.005398.LIANG Jing,LIU Rui,YU Kunjie,et al.Dynamic multiswarm particle swarm optimization with cooperative coevolution for large scale global optimization[J].Journal of Software,2018,29(9):2595-2605.doi:10.13328/j.cnki.jos.005398.
[4]罗家祥,倪晓晔,胡跃明.融合多种搜索策略的差分进化大规模优化算法[J].华南理工大学学报:自然科学版,2017,45(3):97-103.doi:10.3969/j.issn.1000-565X.2017.03.014.LUO Jiaxiang,NI Xiaoye,and HU Yueming.A hybrid differential evolution algorithm with multiple search strategies for large-scale optimization[J].Journal of South China University of Technology:Natural Science Edition,2017,45(3):97-103.doi:10.3969/j.issn.1000-565X.2017.03.014.
[5]MIRJALILI S and LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.doi:10.1016/j.advengsoft.2016.01.008.
[6]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.doi:10.12011/1000-6788(2017)11-2983-12.LONG Wen,CAI Shaohong,JIAO Jianjun,et al.Improved whale optimization algorithm for large scale optimization problems[J].Systems Engineering-Theory&Practice,2017,37(11):2983-2994.doi:10.12011/1000-6788(2017)11-2983-12.
[7]MIRJALILI S,MIRJALILI S M,and LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61.doi:10.1016/j.advengsoft.2013.12.007.
[8]姜天华.混合灰狼优化算法求解柔性作业车间调度问题[J].控制与决策,2018,33(3):503-508.doi:10.13195/j.kzyjc.2017.0124.JIANG Tianhua.Flexible job shop scheduling problem with hybrid grey wolf optimization algorithm[J].Control and Decision,2018,33(3):503-508.doi:10.13195/j.kzyjc.2017.0124.
[9]梁静,刘睿,瞿博阳,等.进化算法在大规模优化问题中的应用综述[J].郑州大学学报:工学版,2018,39(3):15-21.doi:10.13705/j.issn.1671-6833.2017.06.016.LIANG Jing,LIU Rui,QU Boyang,et al.A survey of evolutionary algorithms for large scale optimization problem[J].Journal of Zhengzhou University:Engineering Science,2018,39(3):15-21.doi:10.13705/j.issn.1671-6833.2017.06.016.
[10]MIRJALILI S,MIRJALILI S M,and HATAMLOU A.Multi-verse optimizer:A nature-inspired algorithm for global optimization[J].Neural Computing and Applications,2016,27(2):495-513.doi:10.1007/s00521-015-1870-7.
[11]赵世杰,高雷阜,徒君,等.耦合横纵向个体更新策略的改进MVO算法[J].控制与决策,2018,33(8):1422-1428.doi:10.13195/j.kzyjc.2017.0441.ZHAO Shijie,GAO Leifu,TU Jun,et al.Improved multi verse optimizer coupling horizontal-and-vertical individual updated strategies[J].Control and Decision,2018,33(8):1422-1428.doi:10.13195/j.kzyjc.2017.0441.
[12]CHOPRA N and SHARMA J.Multi-objective optimum load dispatch using Multi-verse optimization[C].Proceedings of the 2016 IEEE 1st International Conference on Power Electronics,Intelligent Control and Energy Systems,Delhi,India,2016:1-5.doi:10.1109/ICPEICES.2016.7853482.
[13]HU Cong,LI Zhi,ZHOU Tian,et al.A multi-verse optimizer with levy flights for numerical optimization and its application in test scheduling for network-on-chip[J].PLOS One,2016,11(12):e0167341.doi:10.1371/journal.pone.0167341.
[14]FARIS H,ALJARAH I,and MIRJALILI S.Training feedforward neural networks using multi-verse optimizer for binary classification problems[J].Applied Intelligence,2016,45(2):322-332.doi:10.1007/s10489-016-0767-1.
[15]JANGIR P,PARMAR S A,TRIVEDI I N,et al.A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem[J].Engineering Science and Technology,An International Journal,2017,20(2):570-586.doi:10.1016/j.jestch.2016.10.007.
[16]ALI E E,EL-HAMEED M A,EL-FERGANY A A,et al.Parameter extraction of photovoltaic generating units using multi-verse optimizer[J].Sustainable Energy Technologies and Assessments,2016,17:68-76.doi:10.1016/j.seta.2016.08.004.
[17]MIRJALILI S.Dragonfly algorithm:A new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4):1053-1073.doi:10.1007/s00521-015-1920-1.
[18]ASKARZADEH A.A novel metaheuristic method for solving constrained engineering optimization problems:Crow search algorithm[J].Computers&Structures,2016,169:1-12.doi:10.1016/j.compstruc.2016.03.001.
[2]BOLUFé-R?HLER A,FIOL-GONZáLEZ S,and CHEN S.A minimum population search hybrid for large scale global optimization[C].Proceedings of 2015 IEEE Congress on Evolutionary Computation,Sendai,Japan,2015:1958-1965.doi:10.1109/CEC.2015.7257125.
[3]梁静,刘睿,于坤杰,等.求解大规模问题协同进化动态粒子群优化算法[J].软件学报,2018,29(9):2595-2605.doi:10.13328/j.cnki.jos.005398.LIANG Jing,LIU Rui,YU Kunjie,et al.Dynamic multiswarm particle swarm optimization with cooperative coevolution for large scale global optimization[J].Journal of Software,2018,29(9):2595-2605.doi:10.13328/j.cnki.jos.005398.
[4]罗家祥,倪晓晔,胡跃明.融合多种搜索策略的差分进化大规模优化算法[J].华南理工大学学报:自然科学版,2017,45(3):97-103.doi:10.3969/j.issn.1000-565X.2017.03.014.LUO Jiaxiang,NI Xiaoye,and HU Yueming.A hybrid differential evolution algorithm with multiple search strategies for large-scale optimization[J].Journal of South China University of Technology:Natural Science Edition,2017,45(3):97-103.doi:10.3969/j.issn.1000-565X.2017.03.014.
[5]MIRJALILI S and LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.doi:10.1016/j.advengsoft.2016.01.008.
[6]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.doi:10.12011/1000-6788(2017)11-2983-12.LONG Wen,CAI Shaohong,JIAO Jianjun,et al.Improved whale optimization algorithm for large scale optimization problems[J].Systems Engineering-Theory&Practice,2017,37(11):2983-2994.doi:10.12011/1000-6788(2017)11-2983-12.
[7]MIRJALILI S,MIRJALILI S M,and LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61.doi:10.1016/j.advengsoft.2013.12.007.
[8]姜天华.混合灰狼优化算法求解柔性作业车间调度问题[J].控制与决策,2018,33(3):503-508.doi:10.13195/j.kzyjc.2017.0124.JIANG Tianhua.Flexible job shop scheduling problem with hybrid grey wolf optimization algorithm[J].Control and Decision,2018,33(3):503-508.doi:10.13195/j.kzyjc.2017.0124.
[9]梁静,刘睿,瞿博阳,等.进化算法在大规模优化问题中的应用综述[J].郑州大学学报:工学版,2018,39(3):15-21.doi:10.13705/j.issn.1671-6833.2017.06.016.LIANG Jing,LIU Rui,QU Boyang,et al.A survey of evolutionary algorithms for large scale optimization problem[J].Journal of Zhengzhou University:Engineering Science,2018,39(3):15-21.doi:10.13705/j.issn.1671-6833.2017.06.016.
[10]MIRJALILI S,MIRJALILI S M,and HATAMLOU A.Multi-verse optimizer:A nature-inspired algorithm for global optimization[J].Neural Computing and Applications,2016,27(2):495-513.doi:10.1007/s00521-015-1870-7.
[11]赵世杰,高雷阜,徒君,等.耦合横纵向个体更新策略的改进MVO算法[J].控制与决策,2018,33(8):1422-1428.doi:10.13195/j.kzyjc.2017.0441.ZHAO Shijie,GAO Leifu,TU Jun,et al.Improved multi verse optimizer coupling horizontal-and-vertical individual updated strategies[J].Control and Decision,2018,33(8):1422-1428.doi:10.13195/j.kzyjc.2017.0441.
[12]CHOPRA N and SHARMA J.Multi-objective optimum load dispatch using Multi-verse optimization[C].Proceedings of the 2016 IEEE 1st International Conference on Power Electronics,Intelligent Control and Energy Systems,Delhi,India,2016:1-5.doi:10.1109/ICPEICES.2016.7853482.
[13]HU Cong,LI Zhi,ZHOU Tian,et al.A multi-verse optimizer with levy flights for numerical optimization and its application in test scheduling for network-on-chip[J].PLOS One,2016,11(12):e0167341.doi:10.1371/journal.pone.0167341.
[14]FARIS H,ALJARAH I,and MIRJALILI S.Training feedforward neural networks using multi-verse optimizer for binary classification problems[J].Applied Intelligence,2016,45(2):322-332.doi:10.1007/s10489-016-0767-1.
[15]JANGIR P,PARMAR S A,TRIVEDI I N,et al.A novel hybrid particle swarm optimizer with multi verse optimizer for global numerical optimization and optimal reactive power dispatch problem[J].Engineering Science and Technology,An International Journal,2017,20(2):570-586.doi:10.1016/j.jestch.2016.10.007.
[16]ALI E E,EL-HAMEED M A,EL-FERGANY A A,et al.Parameter extraction of photovoltaic generating units using multi-verse optimizer[J].Sustainable Energy Technologies and Assessments,2016,17:68-76.doi:10.1016/j.seta.2016.08.004.
[17]MIRJALILI S.Dragonfly algorithm:A new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4):1053-1073.doi:10.1007/s00521-015-1920-1.
[18]ASKARZADEH A.A novel metaheuristic method for solving constrained engineering optimization problems:Crow search algorithm[J].Computers&Structures,2016,169:1-12.doi:10.1016/j.compstruc.2016.03.001.