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Efficient search techniques using adaptive discretization of design variables on real-coded evolutionary computations

Published: 02 July 2018 Publication History

Abstract

In this paper, we evaluate the effects of adaptive discretization of design variables in real-coded evolutionary computations (RCECs). While the appropriate granularity of design variables can improve convergence in RCECs, it is difficult to decide the appropriate one in advance in most of the practical optimization problems. Besides, when the granularity is too coarse, the diversity may be lost. To address these difficulties, we propose two adaptive discretization techniques that discretize each design variable using granularity determined according to the indicator of solution distribution state in design space. In this study, standard deviation(SD) or estimated probability density function(ePDF) is used as an indicator for determining granularities of design variables. We use NSGA-II as an RCEC and thirteen benchmark problems including engineering problems. The generational distance (GD) and inverted generational distance (IGD) metrics are used for investigating the performance of convergence and diversity, respectively. To make sure the statistical difference of results, the Wilcoxon rank-sum test and Welch's t-test are applied in each problem. The results of experiments show that both of the proposed methods can automatically improve convergence in many problems. In addition, it is confirmed that the diversity is also maintained.

References

[1]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (April 2002), 182--197.
[2]
Kalyanmoy Deb and J Sundar. 2006. Reference point based multi-objective optimization using evolutionary algorithms. In Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, 635--642.
[3]
K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. 2002. Scalable multi-objective optimization test problems. In Proceedings of IEEE Congress on Evolutionary Computation CEC '02, Vol. 1. 825--830.
[4]
S. Huband, P. Hingston, L. Barone, and L. While. 2006. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10, 5 (Oct. 2006), 477--506.
[5]
K. Ikeda, H. Kita, and S. Kobayashi. 2001. Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal?. In Proceedings of IEEE Congress on Evolutionary Computation, Vol. 2. 957--962.
[6]
Hisao Ishibuchi, Masakazu Yamane, and Yusuke Nojima. 2012. Effects of Discrete Objective Functions with Different Granularities on the Search Behavior of EMO Algorithms. In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO '12). 481--488.
[7]
A. Lopez Jaimes and C. A. Coello Coello. 2005. MRMOGA: parallel evolutionary multiobjective optimization using multiple resolutions. In Proceedings of IEEE Congress on Evolutionary Computation, Vol. 3. 2294--2301.
[8]
Himanshu Jain and Kalyamoy Deb. 2014. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach. IEEE Transactions on Evolutionary Computation 18, 4 (Aug. 2014), 602--622.
[9]
I.Y. Kim and O.L. de Weck. 2005. Variable chromosome length genetic algorithm for progressive refinement in topology optimization. Structural and Multidisciplinary Optimization 29, 6 (June 2005), 445.
[10]
T. Kondoh, T. Tatsukawa, A. Oyama, T. Watanabe, and K. Fujii. 2016. Effects of discrete de sign-variable precision on real-coded Genetic Algorithm. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 1--8.
[11]
Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, and Eckart Zitzler. 2002. Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation 10, 3 (Sept. 2002), 263--282.
[12]
BSP Mishra, Satchidanand Dehuri, and Sung-Bae Cho. 2015. Swarm Intelligence in Multiple and Many Objectives Optimization: A Survey and Topical Study on EEG Signal Analysis. In Multi-objective Swarm Intelligence. Springer, 27--73.
[13]
A. Oyama, T. Nonomura, and K. Fujii. 2010. Data Mining of Pareto-Optimal Transonic Airfoil Shapes Using Proper Orthogonal Decomposition. Journal of Aircraft 47 (2010), 1756--1762.
[14]
Qingfu Zhang, Aimin Zhou, Shizheng Zhao, Ponnuthurai Nagaratnam Suganthan, Wudong Liu, and Santosh Tiwari. 2008. Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report 264 (2008).

Cited By

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  • (2019)Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multi-objective Optimization Algorithms2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002906(1826-1833)Online publication date: Dec-2019
  • (2019)Automated and Surrogate Multi-Resolution Approaches in Genetic Algorithms2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002659(2066-2073)Online publication date: Dec-2019

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
July 2018
1578 pages
ISBN:9781450356183
DOI:10.1145/3205455
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 July 2018

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  1. evolutionary computations
  2. multi-objective optimization

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View all
  • (2019)Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multi-objective Optimization Algorithms2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002906(1826-1833)Online publication date: Dec-2019
  • (2019)Automated and Surrogate Multi-Resolution Approaches in Genetic Algorithms2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002659(2066-2073)Online publication date: Dec-2019

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