基于自適應(yīng)截?cái)嗖呗缘募s束多目標(biāo)優(yōu)化算法

畢曉君; 張磊

doi:10.11999/JEIT151237

基于自適應(yīng)截?cái)嗖呗缘募s束多目標(biāo)優(yōu)化算法

doi: 10.11999/JEIT151237

畢曉君¹,
張磊¹

基金項(xiàng)目:

國(guó)家自然科學(xué)基金資助項(xiàng)目(61175126)

計(jì)量
- 文章訪問(wèn)數(shù): 1178
- HTML全文瀏覽量: 148
- PDF下載量: 389
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-11-05
- 修回日期: 2016-03-17
- 刊出日期: 2016-08-19

Constrained Multi-objective Optimization Algorithm with Adaptive Truncation Strategy

BI Xiaojun¹,
ZHANG Lei¹

Funds:

The National Natural Science Foundation of China (61175126)

摘要

摘要: 為提高約束多目標(biāo)優(yōu)化問(wèn)題所求解集的分布性和收斂性，該文提出基于自適應(yīng)截?cái)嗖呗缘募s束多目標(biāo)優(yōu)化算法。首先，自適應(yīng)截?cái)噙x擇策略能夠保留Pareto最優(yōu)解和約束違反度及目標(biāo)函數(shù)值均較優(yōu)的不可行解，不僅提高了種群多樣性，而且能夠較好地兼顧多樣性和收斂性；其次，為增強(qiáng)算法的局部開(kāi)發(fā)能力，在變異操作和交叉操作之后進(jìn)行指數(shù)變異；最后，改進(jìn)的擁擠密度估計(jì)方式只選擇一部分Pareto最優(yōu)解和距離較近的個(gè)體參與計(jì)算，不僅更加準(zhǔn)確地反映解集的分布性，而且降低了計(jì)算量。通過(guò)在標(biāo)準(zhǔn)測(cè)試問(wèn)題(CTP系列)上與其他4種優(yōu)秀算法的對(duì)比結(jié)果可以得出，該算法所求解集的分布性和收斂性均得到一定提高，而且相較于對(duì)比算法在求解性能上具備一定的優(yōu)勢(shì)。
- 約束多目標(biāo)優(yōu)化 /
- 約束處理技術(shù) /
- 截?cái)?/a> /
- 分布性 /
- 收斂性
Abstract: To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents a constrained multi-objective optimization algorithm based on adaptive truncation strategy. Firstly, through the proposed truncation strategy, the Pareto optimal solutions and the infeasible solutions with low constraint violation and good objective function values are retained to improve diversity. Besides, both diversity and convergence are coordinated. Secondly, the exponential variation is added for further enhancing the local exploitation ability after mutation and crossover operation. Finally, the improved crowding density estimation chooses a part of the Pareto optimal individuals and the near individuals to take part in the calculation, thus it not only assesses the distribution of the solution set more accurately, but also reduces the computational quantity. The comparative experiment results with another four excellent constrained multi- objective algorithms on the standard constrained multi-objective optimization problems (CTP series) show that diversity and convergence of the proposed algorithm are improved, and it has certain advantages compared with these algorithms.
- Constrained multi-objective optimization /
- Constraint handling technique /
- truncation /
- Distribution /
- Convergence

HTML全文

參考文獻(xiàn)(18)

WEI H. Design exploration of three-dimensional transverse jet in a supersonic crossflow based on data mining and multi-objective design optimization approaches[J]. International Journal of Hydrogen Energy, 2014, 39(8): 3914-3925. doi: 10.1016/j.ijhydene.2013.12.129.

ABDELKHALEK O, KRICHEN S, and GUITOUNI A. A genetic algorithm based decision support system for the multi-objective node placement problem in next wireless generation network[J]. Applied Soft Computing, 2015, 33(8): 278-291. doi: 10.1016/j.asoc.2015.03.034.

PARENTE M, CORTEZ P, and CORREIA A G. An evolutionary multi-objective optimization system for earthworks[J]. Expert Systems with Applications, 2015, 42(19): 6674-6685.

GRASSO R, COCOCCIONI M, MOURRE B, et al. A decision support system for optimal deployment of sonobuoy networks based on sea current forecasts and multi-objective evolutionary optimization[J]. Expert Systems with Applications, 2013, 40(10): 3886-3899. doi: 10.1016/j.eswa. 2012.12.080.

孟紅云, 張小華, 劉三陽(yáng). 用于約束多目標(biāo)優(yōu)化問(wèn)題的雙種群差分進(jìn)化算法[J]. 計(jì)算機(jī)學(xué)報(bào), 2008, 31(2): 229-235.

MENG Hongyun, ZHANG Xiaohua, and LIU Sanyang. A differential evolution based on double population for constrained multi-objective optimization problems[J]. Chinese Journal of Computers, 2008, 31(2): 229-235.

WOLDESENBET Y G, YEN G G, and TESSEMA B G. Constraint handling in multi-objective evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(3): 514-525. doi: 10.1109/TEVC. 2008.2009032.

張勇, 鞏敦衛(wèi), 任永強(qiáng), 等. 用于約束優(yōu)化的簡(jiǎn)潔多目標(biāo)微粒群優(yōu)化算法.電子學(xué)報(bào), 2011, 39(6): 1437-1440.

ZHANG Yong, GONG Dunwei, REN Yongqiang, et al. Barebones multi-objective particle swarm optimizer for constrained optimization problems[J]. Acta Electronica Sinica, 2011, 39(6): 1437-1440.

LONG Q. A constraint handling technique for constrained multi-objective genetic algorithm[J]. Swarm and Evolutionary Computation, 2014, 15(4): 66-79. doi: 10.1016/j. swevo.2013.12.002.

GAO W F, YEN G G, and LIU S Y. A dual-population differential evolution with coevolution for constrained optimization[J]. IEEE Transactions on Cybernetics, 2015, 45(5): 1094-1107. doi: 10.1109/TCYB.2014.2345478.

JAN M A and KHANUM R A. A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D[J]. Applied Soft Computing, 2013, 13(1): 128-148. doi: 10.1016/j.asoc.2012.07.027.

畢曉君, 王玨, 李博, 等. 基于動(dòng)態(tài)遷移的約束生物地理學(xué)優(yōu)化算法[J]. 計(jì)算機(jī)研究與發(fā)展, 2014, 3(3): 580-589.

BI Xiaojun, WANG Jue, LI Bo, et al. An constrained biogeography-based optimization with dynamic migration[J] Journal of Computer Research and Development, 2014, 3(3): 580-589. [12] 鄒德旋, 高立群, 段納. 用修正的差分進(jìn)化算法確定光電模型參數(shù)[J]. 電子與信息學(xué)報(bào), 2014, 36(10): 2521-2525. doi: 10.3724/ SP.J.1146.2013.01858.

ZOU Dexuan, GAO Liqun, and DUAN Na. Determining the parameters of photovoltaic modules by a modified differential evolution algorithm[J]. Journal of Electronics Information Technology, 2014, 36(10): 2521-2525. doi: 10.3724/SP.J. 1146.2013.01858.

TAN Y Y, JIAO Y C, LI H, et al. A modification to MOEA/D-DE for multi-objective optimization problems with complicated Pareto sets[J]. Information Sciences, 2012, 213(23): 14-38.

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.

DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)