一种基于膜系统理论的多目标演化算法

韩敏; 刘闯; 邢军

doi:10.3724/SP.J.1004.2014.00431

一种基于膜系统理论的多目标演化算法

doi: 10.3724/SP.J.1004.2014.00431

韩敏^1, ,,
刘闯¹,
邢军²

1.
大连理工大学电子信息与电气工程学部辽宁大连 116023;
2.
大连工业大学信息科学与工程学院辽宁大连 116034

基金项目:

国家自然科学基金（61074096）资助

详细信息

作者简介:
刘闯大连理工大学博士研究生. 主要研究方向为膜计算, 智能技术, 优化算法,复杂工业系统建模的研究.E-mail：chuang.liu@mail.dlut.edu.cn

通讯作者:
韩敏

计量
- 文章访问数: 1585
- HTML全文浏览量: 121
- PDF下载量: 1574
- 被引次数: 0
出版历程
- 收稿日期: 2013-03-07
- 修回日期: 2013-08-01
- 刊出日期: 2014-03-20

A Multi-objective Evolutionary Algorithm Based on Membrane System Theory

HAN Min^{1
, ,},
LIU Chuang¹,
XING Jun²

1.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023;
2.
Faculty of the Information Science and Engineering, Dalian Polytechnic University, Dalian 116034

Funds:

Supported by National Natural Science Foundation of China (61074096)

摘要

摘要: 提出一种用于求解多目标优化问题的基于膜系统理论的演化算法. 受膜系统理论的功能和处理化合物方式的启发，设计了求解多目标优化问题的演化操作. 此外，在表层膜中，引入了非支配排序和拥挤距离两种机制改善算法的搜索效率. 采用ZDT（Zitzler-Deb-Thiele）和DTLZ（Deb-Thiele-Laumanns-Zitzler）多目标问题对所提算法进行测试，所提算法求得的候选解既能较好地逼近真实Pareto前沿，又能满足非支配解集多样性的要求. 仿真结果表明，所提方法求解多目标优化问题是可行和有效的.
- 膜计算 /
- P系统 /
- 膜算法 /
- 多目标优化算法 /
- Pareto前沿
Abstract: In this paper, an evolutionary algorithm based on the membrane system theory has been proposed for multi-objective optimization problems. Inspired by the function and the compound reaction of the membrane system theory, the evolutionary operators are designed to solve the multi-objective optimization problems. In addition, the non-dominated sorting and the crowding distance are introduced into the skin membrane to improve the search efficiency of the algorithm. The multi-objective optimization problems including ZDT (Zitzler-Deb-Thiele) and DTLZ (Deb-Thiele-Laumanns-Zitzler) are employed to evaluate the performance of the algorithm. The proposed algorithm can not only obtain quickly the approximate Pareto front but also satisfy the requirement of diversity of Pareto front. Simulation results indicate that the proposed algorithm is feasible and effective.
- Membrane computing /
- P system /
- membrane algorithm /
- multi-objective optimization algorithm /
- Pareto front

HTML全文

参考文献(28)

[1]	Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197
[2]	[2] Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271
[3]	[3] Tang L X, Wang X P. A hybrid multiobjective evolutionary algorithm for multiobjective optimization Problems. IEEE Transactions on Evolutionary Computation, 2013, 17(1): 20-45
[4]	[4] Shir O M, Emmerich M, Bck T. Adaptive Niche radii and Niche shapes approaches for Niching with the CMA-ES. Evolutionary Computation, 2010, 18(1): 97-126
[5]	[5] Deb K, Sinha A, Korhonen P J, Wallenius J. An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. IEEE Transactions on Evolutionary Computation, 2010, 14(5): 723-739
[6]	[6] Karahan I, Koksalan M. A territory defining multiobjective evolutionary algorithms and preference incorporation. IEEE Transactions on Evolutionary Computation, 2010, 14(4): 636-664
[7]	[7] Sierra M R, Coello Coello C A. Improving PSO-based multi-objective optimization using crowding, mutation and in-dominance. In: Proceedings of the 3rd Conference on Evolutionary Multi-Criterion Optimization. Berlin, Heidelberg: Springer, 2005, 3410: 505-519
[8]	[8] Das S, Suganthan P N. Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 2011, 15(1): 4-31
[9]	[9] Gong M G, Jiao L C, Du H F, Bo L F. Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, 2008, 16(2): 225-255
[10]	Liu D S, Tan K C, Goh C K, Ho W K. A multiobjective memetic algorithm based on particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B-Cybernetics, 2007, 37(1): 42-50
[11]	Păun G, Rozenberg G, Salomaa A. The Oxford Handbook of Membrane Computing. Oxford: Oxford University Press, 2010
[12]	Păun G. Computing with membranes. Journal of Computer and System Sciences, 2000, 61(1): 108-143
[13]	Ishdorj T O, Leporati A, Pan L Q, Zeng X X, Zhang X Y. Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources. Theoretical Computer Science, 2010, 411(25): 2345-2358
[14]	Pan L Q, Wang J, Hoogeboom H J. Spiking neural P systems with astrocytes. Neural Computation, 2011, 24(3): 805-825
[15]	Pan L Q, Zeng X X, Zhang X Y. Time-free spiking neural P systems. Neural Computation, 2011, 23(5): 1320-1342
[16]	Zhang Ge-Xiang, Pan Lin-Qiang. A survey of membrane computing as a new branch of natural computing. Chinese Journal of Computers, 2010, 33(2): 208-214 (张葛祥, 潘林强. 自然计算的新分支-膜计算. 计算机学报, 2010, 33(2): 208-214)
[17]	Nishida T Y. An approximate algorithm for NP-complete optimization problems exploiting P-systems. In: Proceedings of Brainstorming Workshop on Uncertainty in Membrane Computing. Palma de Majorca, 2004. 185-192
[18]	Zaharie D, Ciobanu G. Distributed evolutionary algorithms inspired by membranes in solving continuous optimization problems. In: Proceedings of the 7th International Conference on Membrane Computing. 2006, 4361: 536-553
[19]	Huang Liang, He Xiong-Xiong, Wang Ning, Xie Yi. P systems based multi-objective optimization algorithm. Progress in Natural Science, 2007, 17(4): 458-465
[20]	Huang Liang, Wang Ning, Zhao Jin-Hui. Multiobjective optimization for controller design. Acta Automatica Sinica, 2008, 34(4): 472-477
[21]	Zhang G X, Li Y, Gheorghe M. A multi-objective membrane algorithm for knapsack problems. In: Proceedings of the 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). Changsha, China: IEEE, 2010. 604-609
[22]	Liu C, Han M, Wang X Z. A multi-objective evolutionary algorithm based on membrane systems. In: Proceedings of 2011 4th International Workshop on Advanced Computational Intelligence (IWACI). Wuhan, China: IEEE, 2011. 103-109
[23]	Zhang G X, Cheng J X, Gheorghe M, Meng Q. A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Applied Soft Computing, 2013, 13(3): 1528-1542
[24]	Zhao J H, Wang N, Zhou P. Multiobjective bio-inspired algorithm based on membrane computing. In: Proceedings of 2012 International Conference on Computer Science and Information Processing (CSIP). Xi'an, China: IEEE, 2012. 473-477
[25]	Liu Sha-Sha, Dou Quan-Sheng, Fu Kai-Lei. Dynamic membrane computing systems based on self-organizing network broadcasting algorithm. Application Research of Computers, 2012, 29(5): 1821-1824 (刘沙沙, 窦全胜, 伏开磊. 基于动态膜计算系统的自组织网络广播算法. 计算机应用研究, 2012, 29(5): 1821-1824)
[26]	Zhang G X, Gheorghe M, Li Y Q. A membrane algorithm with quantum-inspired subalgorithms and its application to image processing. Natural Computing, 2012, 11(4): 701-717
[27]	Deb K, Saha A. Multimodal optimization using a bi-objective evolutionary algorithm. Evolutionary Computation, 2012, 20(1): 27-62
[28]	Nebro A J, Luna F, Alba E, Dorronsoro B, Durillo J J, Beham A. AbYSS: adapting scatter search to multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2008, 12(4): 439-457