一種基于角度懲罰距離的高維多目標(biāo)進(jìn)化算法

畢曉君; 王朝

doi:10.11999/JEIT170454

一種基于角度懲罰距離的高維多目標(biāo)進(jìn)化算法

doi: 10.11999/JEIT170454

畢曉君¹,
王朝¹

基金項(xiàng)目:

國家自然科學(xué)基金(61175126)，中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(HEUCFP201709)

計(jì)量
- 文章訪問數(shù): 1719
- HTML全文瀏覽量: 237
- PDF下載量: 285
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-05-12
- 修回日期: 2017-09-22
- 刊出日期: 2018-02-19

A Many-objective Evolutionary Algorithm Based on Angle Penalized Distance

BI Xiaojun¹,
WANG Chao¹

Funds:

The National Natural Science Foundation of China (61175126), The Fundamental Research Funds for the Central Universities (HEUCFP201709)

摘要

摘要: 為了使多目標(biāo)進(jìn)化算法在收斂性和分布性之間保持平衡，該文提出一種基于角度懲罰距離的高維多目標(biāo)進(jìn)化算法(Many-Objective Evolutionary Algorithm based on Angle Penalized Distance, MaOEA-APD)。首先，綜合考慮收斂性和分布性在進(jìn)化不同階段的重要性，構(gòu)造一種角度懲罰距離，使兩者隨進(jìn)化進(jìn)程動(dòng)態(tài)平衡；其次，開發(fā)基于刪除劣質(zhì)個(gè)體的環(huán)境選擇策略，在提高種群分布性的同時(shí)提高收斂性；最后，根據(jù)環(huán)境選擇的原理，設(shè)計(jì)與之相協(xié)調(diào)且互補(bǔ)的匹配選擇過程，提高算法的整體進(jìn)化效率。將所提算法與目前國內(nèi)外性能優(yōu)異的3種高維多目標(biāo)進(jìn)化算法進(jìn)行對(duì)比，實(shí)驗(yàn)結(jié)果表明在WFG標(biāo)準(zhǔn)測試函數(shù)集上，該文算法相對(duì)于其他算法，綜合性能有了較大的提升。
- 高維多目標(biāo)優(yōu)化 /
- 進(jìn)化算法 /
- 刪除策略 /
- 角度懲罰距離
Abstract: In order to balance between convergence and distribution in Multi-Objective Evolutionary Algorithms (MOEAs), a Many-Objective Evolutionary Algorithm based on Angle Penalized Distance (MaOEA-APD) is proposed. Firstly, considering the importance of convergence and diversity in the different stages of the evolutionary process, an angle penalized distance is constructed to dynamically balance between them. Then, the environmental selection based on removing the worse individual is designed to maintain the distribution and improve the convergence. Finally, the mating selection is designed based on the principle of the environmental selection. Both are complement and coordinated to each other for improving the evolutionary efficiency of the algorithm. Compared with three state-of-the-art many-objective evolutionary algorithms (MaOEAs), the experimental results on WFG test suite show that MaOEA-APD has more advantage than other algorithms in terms of the overall performance.
- Many-objective optimization /
- Evolutionary algorithm /
- Elimination strategy /
- Angle penalized distance

HTML全文

參考文獻(xiàn)(23)

鞏敦衛(wèi), 季新芳, 孫曉燕. 基于集合的高維多目標(biāo)優(yōu)化問題的進(jìn)化算法[J]. 電子學(xué)報(bào), 2014, 42(1): 77-83. doi: 10.3969/ j.issn.0372-2112.2014.01.012.

GONG Dunwei, JI Xinfang, and SUN Xiaoyan. Solving many-objective optimization problems using set-based evolutionary algorithms[J]. Acta Electronica Sinica, 2014, 42(1): 77-83. doi: 10.3969/j.issn.0372-2112.2014.01.012.

陳小紅, 李霞, 王娜. 高維多目標(biāo)優(yōu)化中基于稀疏特征選擇的目標(biāo)降維方法[J]. 電子學(xué)報(bào), 2015, 43(7): 1300-1307. doi: 10.3969/j.issn.0372-2112.2015.07.008.

CHEN Xiaohong, LI Xia, and WANG Na. Objective reduction with sparse feature selection for many objective optimization problem[J]. Acta Electronica Sinica, 2015, 43(7): 1300-1307. doi: 10.3969/j.issn.0372-2112.2015.07.008.

HUGHES E J. Radar waveform optimisation as a many- objective application benchmark[C]. International Conference on Evolutionary Multi-Criterion Optimization, Springer-Verlag, 2007: 700-714. doi: 10.1007/978-3-540- 70928-2_53.

REED P M and KOLLAT J B. Save now, pay later? Multi- period many-objective groundwater monitoring design given systematic model errors and uncertainty[J]. Advances in Water Resources, 2012, 35: 55-68. doi: 10.1016/j.advwatres. 2011.10.011.

LYGOE R J, CARY M, and FLEMING P J. A Real-World Application Of A Many-Objective Optimisation Complexity Reduction Process[M]. Evolutionary Multi-Criterion Optimization, Springer Berlin Heidelberg, 2013: 641-655.

孔維健, 丁進(jìn)良, 柴天佑. 高維多目標(biāo)進(jìn)化算法研究綜述[J]. 控制與決策, 2010, 25(3): 321-326. doi: 10.13195/j.cd.2010. 03.4.kongwj.008.

KONG Weijian, DING Jinliang, and CHAI Tianyou. Survey on large-dimensional multi-objective evolutionary algorithms [J]. Control Decision, 2010, 25(3): 321-326. doi: 10.13195 /j.cd.2010.03.4.kongwj.008.

LI Ke, DEB K, ZHANG Q, et al. An evolutionary many- objective optimization algorithm based on dominance and decomposition[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(5): 694-716. doi: 10.1109/TEVC.2014. 2373386.

CHENG Jixiang, YEN G G, and ZHANG G. A many- objective evolutionary algorithm with enhanced mating and environmental selections[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(4): 592-605. doi: 10.1109 /TEVC.2015.2424921.

ZHANG Xingyi, TIAN Ye, and JIN Yaochu. A knee point-driven evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2015, 19(6): 761-776. doi: 10.1109/TEVC. 2014.2378512.

陳振興, 嚴(yán)宣輝, 吳坤安, 等. 融合張角擁擠控制策略的高維多目標(biāo)優(yōu)化[J]. 自動(dòng)化學(xué)報(bào), 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555.

CHEN Zhenxing, YAN Xuanhui, WU Kunan, et al. Many- objective optimization integrating open angle based congestion control strategy[J]. Acta Automatica Sinica, 2015, 41(6): 1145-1158. doi: 10.16383/j.aas.2015.c140555.

CHENG Ran, JIN Yaochu, OLHOFER M, et al. A reference vector guided evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5): 773-791. doi: 10.1109/TEVC.2016. 2519378.

HE Zhenan and YEN G G. Many-objective evolutionary algorithms based on coordinated selection strategy[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(2): 220-233. doi: 10.1109/TEVC.2016.2598687.

XIANG Yi, ZHOU Yuren, LI Miqing, et al. A vector angle- based evolutionary algorithm for unconstrained many- objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(1): 131-152. doi: 10.1109 /TEVC.2016.2587808.

LI Miqing, YANG Shengxiang, and LIU Xiaohui. Bi-goal evolution for many-objective optimization problems[J]. Artificial Intelligence, 2015, 228: 45-65.

鄭金華, 申瑞珉, 李密青, 等. 一種基于信息分離的高維多目標(biāo)進(jìn)化算法[J]. 軟件學(xué)報(bào), 2015, 26(5): 1013-1036. doi: 10.13328/j.cnki.jos.004676.

ZHENG Jinhua, SHEN Ruimin, LI Miqing, et al. Evolutionary algorithm based on information separation for many-objective optimization[J]. Journal of Software, 2015, 26(5): 1013-1036. doi: 10.13328/j.cnki.jos.004676.

DEB K and JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601. doi: 10.1109/TEVC.2013. 2281535.

HUBAND S, HINGSTON P, BARONE L, et al. A review of multiobjective test problems and a scalable test problem toolkit[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(5): 477-506. doi: 10.1109/TEVC.2005.861417.

ZITZLER E, THIELE L, LAUMANNS M, et al. Performance assessment of multiobjective optimizers: An analysis and review[J]. IEEE Transactions on Evolutionary Computation, 2003, 7(2): 117-132. doi: 10.1109/TEVC.2003.810758.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計(jì)