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Zhang H, Xiao H, Liu S, Jiao W, Lan C, Ren Z, Wei Y. A Relation B-cell Network used for data identification and fault diagnosis. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Sarafrazi S, Nezamabadi-pour H, Seydnejad SR. A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2015. [DOI: 10.1016/j.jksuci.2014.10.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Minimizing makespan for flow shop scheduling problem with intermediate buffers by using hybrid approach of artificial immune system. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.11.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Cuevas E, Osuna-Enciso V, Oliva D. Circle detection on images based on the Clonal Selection Algorithm (CSA). THE IMAGING SCIENCE JOURNAL 2015. [DOI: 10.1179/1743131x14y.0000000079] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2013.09.001] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Yan X, Zhu Y, Zou W, Wang L. A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.04.025] [Citation(s) in RCA: 108] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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GONG MAOGUO, JIAO LICHENG, YANG JIE, LIU FANG. LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION. INT J ARTIF INTELL T 2011. [DOI: 10.1142/s0218213010000029] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.
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Affiliation(s)
- MAOGUO GONG
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an, Shaanxi 710071, China
| | - LICHENG JIAO
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an, Shaanxi 710071, China
| | - JIE YANG
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an, Shaanxi 710071, China
| | - FANG LIU
- Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an, Shaanxi 710071, China
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GONG WENYIN, CAI ZHIHUA, JIA LIYUAN, LI HUI. A GENERALIZED HYBRID GENERATION SCHEME OF DIFFERENTIAL EVOLUTION FOR GLOBAL NUMERICAL OPTIMIZATION. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2011. [DOI: 10.1142/s1469026811002982] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization over continuous domain, which has been widely used in many areas. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, two operators, the migration operator of biogeography-based optimization and the "DE/best/1" mutation operator, are employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.
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Affiliation(s)
- WENYIN GONG
- School of Computer Science, China University of Geosciences, Wuhan 430074, P. R. China
- State Key Laboratory of Software Engineering, Wuhan University, 430072, P. R. China
| | - ZHIHUA CAI
- School of Computer Science, China University of Geosciences, Wuhan 430074, P. R. China
| | - LIYUAN JIA
- Department of Computer Science, Hunan City University, Yiyang Hunan 413000, P. R. China
| | - HUI LI
- School of Computer Science, China University of Geosciences, Wuhan 430074, P. R. China
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Lukoseviciute K, Ragulskis M. Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems. Neurocomputing 2010. [DOI: 10.1016/j.neucom.2010.02.014] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Gong M, Jiao L, Liu F, Ma W. Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization. Knowl Inf Syst 2009. [DOI: 10.1007/s10115-009-0261-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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