• Articles • Previous Articles     Next Articles

Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization

Ehab Z. Elfeky, Ruhul A. Sarker, and Daryl L. Essam   

  1. School of Information Technology and Electrical Engineering, University of New South Wales, ADFA Campus Canberra 2600, Australia
  • Revised:2007-11-20 Online:2008-01-15 Published:2008-01-10

Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with six other evolutionary based methods, using 12 benchmark problems from the literature, it shows very encouraging performance. T-tests have been applied in this research to show if there is any statistically significance differences between the algorithms. A study has also been carried out in order to show the effect of each component of the proposed algorithm.

Key words: distributed object; mobile computing; framework;



[1]Mezura-Montes E, Coello C A C. A simple multimembered evolution strategy to solve constrained optimization problems. -\it IEEE Trans. Evolutionary Computation}, 2005, 9(1): 1--17.

[2] Barbosa H J C, Lemonge A C C. A new adaptive penalty scheme for genetic algorithms. -\it Inf. Sci.}, 2003, 156(3): 215--251.

[3] Deb K. An efficient constraint handling method for genetic algorithms. -\it Computer Methods in Applied Mechanics and Engineering}, 2000, 186(2-4): 311--338.

[4] Koziel S, Michalewicz Z. Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. -\it Evolutionary Computation}, 1999, 7(1): 19--44.

[5] Farmani R, Wright J A. Self-adaptive fitness formulation for constrained optimization. -\it IEEE Transactions on Evolutionary Computation}, 2003, 7(5): 445--455.

[6] Venkatraman S, Yen G G. A generic framework for constrained optimization using genetic algorithms. -\it IEEE Transactions on Evolutionary Computation}, 2005, 9(4): 424--435.

[7] Runarsson T P, Yao X. Stochastic ranking for constrained evolutionary optimization. -\it IEEE Transactions on Evolutionary Computation}, 2000, 4(3): 284.

[8] Michalewicz Z. Genetic algorithms, numerical optimization, and constraints. In -\it Proc. 6th International Conference on Genetic Algorithms}, San Francisco, CA, 1995, pp.151--158.

[9] Sarker R, Kamruzzaman J, Newton C. Evolutionary optimization (EvOpt): A brief review and analysis. -\it International Journal of Computational Intelligence and Applications}, 2003, 3(4): 311--330.

[10] Eiben A E, Rau\'e P E, Ruttkay Z. Genetic algorithms with multi-parent recombination. In -\it Proc. 3rd Conference on Parallel Problem Solving from Nature}, Jerusalem, 1994, pp.78--87.

[11] Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs. 3rd Rev. and Extended Ed, Berlin; New York: Springer-Verlag, 1996.

[12] Zhao X, Gao X S, Hu Z. Evolutionary programming based on non-uniform mutation. MMRC, AMSS, Chinese Academy of Sciences, \rm Beijing, China, December 2004, No.23, pp.352--374.

[13] Sarker R, Newton C. A comparative study of different penalty function-based GAs for constrained optimization. In -\it Proc. the 4th Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems}, Japan, 2000.

[14] Runarsson T P, Yao X. Search biases in constrained evolutionary optimization. -\it IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews}, 2005, 35(2): 233--243.

[15] Elfeky E Z, Sarker R A, Essam D L. A simple ranking and selection for constrained evolutionary optimization. In -\it Proc. 6th International Conf. Simulated Evolution and Learning}, Hefei, China, 2006, pp.537--544.

[16] Floudas C A, Pardalos P M. A collection of test problems for constrained global optimization algorithms. -\it Lecture Notes in Computer Science}, vol. 455. Berlin: Springer-Verlag, 1990.

[17] Michalewicz Z, Nazhiyath G, Michalewicz M. A note on usefulness of geometrical crossover for numerical optimization problems. In -\it Proc. 5th Annual Conference on Evolutionary Programming}, San Diego, CA, 1996, pp.305--312.

[18] Himmelblau D M. Applied Nonlinear Programming. New York: McGraw-Hill, 1972.

[19] Hock W, Schittkowski K. Text Examples for Nonlinear Programming Codes. New York: Springer-Verlag, 1981.
[1] Zeynep Banu Ozger, Nurgul Yuzbasioglu Uslu. An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples [J]. Journal of Computer Science and Technology, 2021, 36(2): 445-462.
[2] Hong Fang, Bo Zhao, Xiao-Wang Zhang, Xuan-Xing Yang. A United Framework for Large-Scale Resource Description Framework Stream Processing [J]. Journal of Computer Science and Technology, 2019, 34(4): 762-774.
[3] Xi Yang, Gul Jabeen, Ping Luo, Xiao-Ling Zhu, Mei-Hua Liu. A Unified Measurement Solution of Software Trustworthiness Based on Social-to-Software Framework [J]. , 2018, 33(3): 603-620.
[4] Yuhun Jun, Jaemin Lee, Euiseong Seo. Evaluation of Remote-I/O Support for a DSM-Based Computation Offloading Scheme [J]. , 2017, 32(5): 957-973.
[5] Tao Liu, Yi Liu, Qin Li, Xiang-Rong Wang, Fei Gao, Yan-Chao Zhu, De-Pei Qian. SEIP: System for Efficient Image Processing on Distributed Platform [J]. , 2015, 30(6): 1215-1232.
[6] Kai Lu, Xu Zhou, Xiao-Ping Wang, Tom Bergan, Chen Chen. An Efficient and Flexible Deterministic Framework for Multithreaded Programs [J]. , 2015, 30(1): 42-56.
[7] Zhong-Gui Sun, Song-Can Chen, Li-Shan Qiao . A Two-Step Regularization Framework for Non-Local Means [J]. , 2014, 29(6): 1026-1037.
[8] Jie Wu. Collaborative Mobile Charging and Coverage [J]. , 2014, 29(4): 550-561.
[9] Hai-Long Shi, Dong Li, Jie-Fan Qiu, Chen-Da Hou, Li Cui. A Task Execution Framework for Cloud-Assisted Sensor Networks [J]. , 2014, 29(2): 216-226.
[10] Hong-Tao Zhang (张宏涛), Min-Lie Huang (黄民烈), and Xiao-Yan Zhu (朱小燕), Member CCF. A Unified Active Learning Framework for Biomedical Relation Extraction [J]. , 2012, 27(6): 1302-1313.
[11] Claudia Canali, Member, IEEE, Michele Colajanni, Member, IEEE, Delfina Malandrino, Vittorio Scarano, Member, ACM, and Raffaele Spinelli. A Novel Intermediary Framework for Dynamic Edge Service Composition [J]. , 2012, (2): 281-297.
[12] Chao-Sheng Lin (林朝圣), Chun-Hsien Lu (吕俊贤), Shang-Wei Lin (林尚威), Yean-Ru Chen (陈盈如), and Pao-Ann Hsiung (熊博安), Senior Member, ACM, IEEE. VERTAF/Multi-Core: A SysML-Based Application Framework for Multi-Core Embedded Software Development [J]. , 2011, 26(3): 448-462.
[13] Jian-Wei Xu, Student Member, CCF, Ming-Yu Chen, Member, CCF, ACM, IEEE, Gui Zheng, Zheng Cao, Hui-Wei Lv, and Ning-Hui Sun, Senior Member, CCF, Member, IEEE. SimK: A Large-Scale Parallel Simulation Engine [J]. , 2009, 24(6): 1048-1060.
[14] Kwangjin Park, Hyunseung Choo, and Chong-Sun Hwang. An Efficient Data Dissemination Scheme for Spatial Query Processing [J]. , 2007, 22(1): 131-134 .
[15] Jin-Woo Kim, Ju-Hum Kwon, Young-Gab Kim, Chee-Yang Song, Hyun-Seok Kim, and Doo-Kwon Baik. EAFoC: Enterprise Architecture Framework Based on Commonality [J]. , 2006, 21(6): 952-964 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved