A Hybrid Invasive Weed Optimization with Feasibility-Based Rule for Constrained Optimization Problem

• Autorzy: Chen H. , Zhou P. , Guo P. , Ouyang X. , He S. , Zheng H.

Warianty tytułu

PL

Optymalizacja hybrydowa IWO z wykorzystaniem reguł wykonalności w optymalizacji z funkcją kosztu

• Języki publikacji: EN

Abstrakty

EN

During the past decade, hybrid algorithms combining evolutionary computation and constraint-handling techniques is one of the most popular method to solve constrained optimization problems. Usually, penalty functions are often used in constrained optimization. But it is difficult to strike the right balance between objective and penalty functions. As a novel population-based algorithm, invasive weed optimization (IWO) algorithm has gained wide applications in a variety of fields, especially for unconstrained optimization problems. In this paper, a hybrid IWO (HIWO) with a feasibility-based rule is proposed to solve constrained optimization problems. The feasibility-based rule does not need additional parameters, which is different from penalty functions. In addition, the complex method is used to provide direction for weed evolution, which can accelerate the convergence speed. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed HIWO.

PL

W artykule przedstawiono opracowaną metodę optymalizacji z funkcją kosztu, bazującą na hybrydowej metodzie IWO (ang. Hybrid Invasive Weed Optimizastion) oraz regułach związanych z wykonalnością. Zasady wykonalności, w przeciwieństwie do funkcji kar, nie wymagają dodatkowych parametrów. Dodatkowo zastosowano kompleksową metodę określania kierunki ewolucji trawy w algorytmie IWO, co pozwala na przyspieszenie konwergencji. Przeprowadzone badania symulacyjne i porównawcze dowodzą skuteczności i sprawności proponowanej metody HIWO.

Słowa kluczowe

EN

weed control; feasibility; constrained optimization; complex method of diagnosing

PL

IWO; funkcja kosztu; reguła wykonalności

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 4
• Strony: 160--167
• Opis fizyczny: Bibliogr. 31 poz., tab., wykr.

Twórcy

autor

Chen H.

• Guangxi University for Nationalities

autor

Zhou P.

• Guangxi University for Nationalities
• Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis

autor

Guo P.

• Guangxi University for Nationalities

autor

Ouyang X.

• Guangxi University for Nationalities

autor

He S.

• Guangxi University for Nationalities

autor

Zheng H.

• Guangxi University for Nationalities

Bibliografia

• [1] S.J. Wu, P.T. Chow, Genetic algorithms for nonlinear mixed discrete integer optimization problems via meta genetic parameter optimization, Engineering Optimization 24. (1995) 137-159
• [2] K.Deb, Optimal design of a welded beam via genetic algorithms, AIAA Journal, 29.(1991) 2013-2015
• [3] L.S.Coelho, An efficient particle swarm approach for mixedinteger programming in reliability-redundancy optimization applications, Reliability Engineering and System Safety 94. (2009) 830-837
• [4] D.P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods, Academic Press, New York, (1982)
• [5] Homaifar A, C.X. Qi, S.H. Lai, Constrained optimization via genetic algorithms, Simulation 62.(1994) 242–254
• [6] J.A. Joines, C.R. Houck, On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs, in: D. Fogel (Ed.), Proceedings of First IEEE Conference on Evolutionary Computation, IEEE Press, Orlando, FL, (1994),579–584
• [7] Huang F, Wang L, He Q, An effective co-evolutionary differential evolution for constrained optimization, Applied Mathematics and computation 186.(2007). 340−356
• [8] K. Deb. An efficient constraint handling method for genetic algorithms, Comput, Meth, Appl. Mech. Eng, 186. (2000) 311–338
• [9] Qie He, Ling Wang. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Applied Mathematics and computation, 186. (2007),1407-1422
• [10] Runarsson TP, Yao X, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. on Evolutionary Computation, 4.(2000). 284−294
• [11] Coello Coello C A, Constraint-handling using an evolutionary multi-objective optimization technique, Civil Engineering and Environmental Systems, 17. (2000) 319-346
• [12] Mehrabian A R, Lucas C, A novel numerical optimization algorithm inspired from weed colonization, Ecological Informatics, 1. (2006) 355-366
• [13] Ritwik Giri, Aritra Chowdhury, Arnob Ghosh, et al, A modified invasive weed optimization algorithm for training of feedforward neural networks, IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul: IEEE, 2010, pp. 3166-3173
• [14] Huan Zhao, Pei-hong Wang, Xianyong Peng, et al. Constrained optimization of combustion at a coal-fired utility boiler using hybrid particle swarm optimization with invasive weed, 2009 International Conference on Energy and Environment Technology (ICEET), Washington, DC, IEEE Computer Society, (2009). 564-567
• [15] Kundu D, Suresh K, Ghosh S, et a. Designing fractional-order PIλDμ controller using a modified invasive weed optimization algorithm, 2009 World Congress on Nature & Biologically Inspired Computing, (2009). 1315-1320
• [16] Sengupta, A, Chakraborti, T, Konar, A. Energy efficient trajectory planning by a robot arm using invasive weed optimization technique, Third World Congress on Nature and Biologically Inspired Computing, (2011).311-316
• [17] Ho-Lung Hung, Chien-Chi Chao, Chia-Hsin Cheng, Invasive weed optimization method based blind multi-user detection for MC-CDMA interference suppression over multipath fading channel, IEEE International Conference on Systems Man and Cybernetics (SMC), (2010). 2145-2150
• [18] Zhihua Chen, Shuo Wang, Zhonghua Deng, Tuning of autodisturbance rejection controller based on the invasive weed optimization. In 2011 Sixth International Conference on Bio- Inspired Computing: Theories and Applications, (2011). 314-318
• [19] Mohsen Ramezani Ghalenoei, Hossein Hajimirsadeghi, Caro Lucas. Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVS, In 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference, Shanghai, (2009). 1665-1670
• [20] Gourab Ghosh Roy, Swagatam Das, Prithwish Chakraborty, Design of non-uniform circular antenna arrays using a modified invasive weed optimization algorithm, IEEE Transactions on Antennas and Propagation, 59. (2011) 110-118
• [21] Zhang X, Wang Y, Cui G, et al. Application of a novel IWO to the design of encoding sequences for DNA computing, Computers and Mathematics with Applications, 57.(2009) 2001-2008
• [22] Mehrabian A R, Yousefi-Koma A, A novel technique for optimal placement of piezoelectric actuators on smart structure. Journal of the Franklin Institute, 348.(2011) 12-23
• [23] Su Shoubao, Fang Jie, Wang Jiwen, et al, Image clustering method based on invasive weed optimization, Journal of South China University of Technology (Natural Science Edition). 36. (2008) 95-105
• [24] Zhang Q, Chen D D, Qin X R, et al, Convergence analysis of invasive weed optimization algorithm and iIts application in engineering. Journal of Tong Ji University (Natural Science), 38. (2010) 1689-1693
• [25] Su Shoubao, Wang Jiwen, Zhang Ling, et al, An invasive weed optimization algorithm for constrained engineering design problems, Journal of University of Science and Technology of China, 39. (2009) 885-893
• [26] Kozie S, Michalewicz Z. Evolutionary algorithms, homorphous mapping, and constrained parameter optimization, Evolutionary Computation, 7. (1999) 19-44
• [27] Farmani R, Wright J A. Self-adaptive fitness formulation for constrained optimization. IEEE Transactions on Evolutionary Computation, 7.(2003) 445-455
• [28] Mezura-Montes E, Coello Coello C A. A simple multimembered evolution strategy to solve constrained optimization problems, IEEE Transactions on Evolutionary Computation, 9. (2005) 1-17
• [29] Jiao L C, Shang R H, Mang W P, et al, Multi-objective optimization immune algorithm, theory and application, Science Press, Beijing, China (2010)
• [30] Yongquan Zhou, Jiakun Liu. Leader glowworm swarm optimization algorithm for solving nonlinear equations system, Przeglad Elektrotchniczny,1b.(2012)101-106
• [31] Yongquan Zhou, Zhe ouyang, Jiakun Liu. A novel K-means image clustering algorithm based on glowworm swarm optimization. Przeglad Elektrotchniczny,8.(2012)266-270