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An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Authors:

Yongming Cai Academic Editor:

Mario VersaciAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2021

https://doi.org/10.1155/2021/8928182

Published: 01 January 2021 Publication History

Abstract

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

References

[1]

G. Dhiman and V. Kumar, “Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications,” Advances in Engineering Software, vol. 114, pp. 48–70, 2017.

[2]

M. Ghaemi and M.-R. Feizi-Derakhshi, “Forest optimization algorithm,” Expert Systems with Applications, vol. 41, no. 15, pp. 6676–6687, 2014.

Digital Library

[3]

J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference Neural Network, pp. 1942–1948, Perth, Western Australia, November 1995.

[4]

S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016.

Digital Library

[5]

D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Erciyes University, Kayseri, Turkey, 2005, Technical Report TR06.

[6]

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, 2014.

Digital Library

[7]

S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper optimisation algorithm: theory and application,” Advances in Engineering Software, vol. 105, pp. 30–47, 2017.

Digital Library

[8]

R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-Learning-Based Optimization: an optimization method for continuous non-linear large scale problems,” Information Sciences, vol. 183, no. 1, pp. 1–15, 2012.

Digital Library

[9]

D. Tang, S. Dong, L. He, and Y. Jiang, “Intrusive tumor growth inspired optimization algorithm for data clustering,” Neural Computing & Applications, vol. 27, no. 2, pp. 349–374, 2016.

Digital Library

[10]

S. A. Uymaz, G. Tezel, and E. Yel, “Artificial algae algorithm (AAA) for nonlinear global optimization,” Applied Soft Computing, vol. 31, pp. 153–171, 2015.

Digital Library

[11]

S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, pp. 163–191, 2 017.

Digital Library

[12]

C. K. H. Lee, “A review of applications of genetic algorithms in operations management,” Engineering Applications of Artificial Intelligence, vol. 76, pp. 1–12, 2018.

[13]

J. Grefenstette, “Optimization of control parameters for genetic algorithms,” IEEE Transactions on systems, man, and cybernetics, vol. 16, no. 1, pp. 122–128, 1986.

Digital Library

[14]

T. P. Pawlak, “Synthesis of Mathematical Programming Models with One-Class Evolutionary Strategies,” Swarm and Evolutionary Computation, vol. 44, pp. 1–14, 2018.

[15]

A. R. Hota and A. Pat, “An adaptive quantum-inspired differential evolution algorithm for 0-1 knapsack problem,” in Proceedings of the 2nd World Congresson Nature and Biologically Inspired Computing (NaBIC’10), pp. 703–708, Kitakyushu, Japan, December 2010.

[16]

X. Yao and Y. Liu, “Fast evolutionary programming,” Evolutionary Programming, vol. 3, pp. 451–460, 1996.

[17]

L. L. Lai and J. T. Ma, “Application of evolutionary programming to reactive power planning−comparison with nonlinear programming approach,” IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 198–206, 1997.

[18]

R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.

Digital Library

[19]

R. Tanabe and A. Fukunaga, “Success-history Based Parameter Adaptation for Differential Evolution,” in Proceedings of the 2013 IEEE Congress on Evolutionary Computation, pp. 71–78, Cancun, Mexico, December 2013.

[20]

R. Tanabe and A. S. Fukunaga, “Improving the Search Performance of SHADE Using Linear Population Size Reduction,” in Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665, Beijing, China, July 2014.

[21]

J. Brest, M. Sepesy Maucec, and B. Boskovic, “Improved L-SHADE Algorithm for Single Objective Real-Parameter Optimization,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1188–1195, Vancouver, Canada, July 2016.

[22]

A. Kaveh and A. Dadras, “A novel meta-heuristic optimization algorithm: thermal exchange optimization,” Advances in Engineering Software, vol. 110, pp. 69–84, 2017.

Digital Library

[23]

E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232–2248, 2009.

Digital Library

[24]

A. F. Nematollahi, A. Rahiminejad, and B. Vahidi, “A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization,” Applied Soft Computing, vol. 59, pp. 596–621, 2017.

Digital Library

[25]

A. Hatamlou, “Black hole: a new heuristic optimization approach for data clustering,” Information Sciences, vol. 222, pp. 175–184, 2013.

Digital Library

[26]

A. Kaveh and M. Khayatazad, “A new meta-heuristic method: ray optimization,” Computers & Structures, vol. 112, pp. 283–294, 2012.

Digital Library

[27]

D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, pp. 67–82, 1997.

Digital Library

[28]

L. Xue Hui, Y. Yang, and L. Xia, “Solving TSP with shuffled frog leaping algorithm. Intelligent Systems Design and Applications,” in Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications, Kaohsuing, Taiwan, November 2008.

[29]

J. Luo, X. Li, M.-R. Chen, and H. Liu, “A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows,” Information Sciences, vol. 316, pp. 266–292, 2015.

Digital Library

[30]

M. R. Narimani, “A new modified shuffle frog leaping algorithm for non-smooth economic dispatch,” World Applied Sciences Journal, vol. 12, no. 6, pp. 803–814, 2011.

[31]

P. Roy and A. Chakrabarti, “Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect,” Applied Soft Computing, vol. 13, no. 11, pp. 4244–4252, 2013.

[32]

K. K. Bhattacharjee and S. P. Sarmah, “Shuffled frog leaping algorithm and its application to 0/1 knapsack problem,” Applied Soft Computing, vol. 19, pp. 252–263, 2014.

[33]

L. Wang and C. Fang, “An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem,” Information Sciences, vol. 181, no. 20, pp. 4804–4822, 2011.

Digital Library

[34]

C. Fang and L. Wang, “An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem,” Computers & Operations Research, vol. 39, no. 5, pp. 890–901, 2012.

Digital Library

[35]

D. Lei and X. Guo, “A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents,” Expert Systems with Applications, vol. 42, no. 23, pp. 9333–9339, 2015.

Digital Library

[36]

J. Li, Q. Pan, and S. Xie, “An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems,” Applied Mathematics and Computation, vol. 218, no. 18, pp. 9353–9371, 2012.

[37]

O. Yang and Y. Sun, “Grid task scheduling strategy based on improved shuffled frog leaping algorithm,” Computer Engineering, vol. 37, no. 21, pp. 146–151, 2011.

[38]

X. Li, J. Luo, M.-R. Chen, and N. Wang, “An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation,” Information Sciences, vol. 192, pp. 143–151, 2012.

Digital Library

[39]

M. A. Ahandani and H. Alavi-Rad, “Opposition-based learning in shuffled frog leaping: an application for parameter identification,” Information Sciences, vol. 291, pp. 19–42, 2015.

Digital Library

[40]

M. A. Ahandani, “A diversified shuffled frog leaping: an application for parameter identification,” Applied Mathematics and Computation, vol. 239, pp. 1–16, 2014.

[41]

S. Sharma, T. K. Sharma, M. Pant, J. Rajpurohit, and B. Naruka, “Centroid mutation embedded shuffled frog-leaping algorithm,” Procedia Computer Science, vol. 46, pp. 127–134, 2015.

Digital Library

[42]

H. B. Wang, K. P. Zhang, and X. Y. Tu, “A mnemonic shuffled frog leaping algorithm with cooperation and mutation,” Applied Intelligence, vol. 43, no. 1, pp. 32–48, 2015.

Digital Library

[43]

C. Liu, P. Niu, G. Li, M. Yunpeng, and C. Ke, “Enhanced shuffled frog leaping algorithm for solving numerical function optimization problems,” Journal of Intelligent Manufacturing, vol. 29, no. 5, pp. 1–21, 2015.

Digital Library

[44]

H. Liu, F. Yi, and H. Yang, “Adaptive grouping cloud model shuffled frog leaping algorithm for solving continuous optimization problems,” Computational Intelligence and Neuroscience, vol. 2016, p. 25, 2016.

Digital Library

[45]

D. Tang, J. Yang, S. Dong, and Z. Liu, “A lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems,” Applied Soft Computing, vol. 49, pp. 641–662, 2016.

Digital Library

[46]

W. Li, J. Cao, J. Wu, C. Huang, and R. Buyya, “A collaborative filtering recommendation method based on discrete quantum-inspired shuffled frog leaping algorithms in social networks,” Future Generation Computer Systems, vol. 88, pp. 262–270, 2018.

Digital Library

[47]

J. Sun, B. Feng, and W. B. Xu, “Particle swam optimization with particles having quantum behavior,” IEEE Congress on Evolutionary Computation, vol. 1, pp. 325–331, 2004.

[48]

M. Xi, J. Sun, and W. Xu, “An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 751–759, 2008.

[49]

J. Sun, W. Fang, V. Palade, X. Wu, and W. Xu, “Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point,” Applied Mathematics and Computation, vol. 218, no. 7, pp. 3763–3775, 2011.

[50]

D. Chen, J. Wang, F. Zou, W. Hou, and C. Zhao, “An improved group search optimizer with operation of quantum-behaved swarm and its application,” Applied Soft Computing, vol. 12, no. 2, pp. 712–725, 2012.

Digital Library

[51]

J. Sun, X. Wu, W. Fang, Y. Ding, H. Long, and W. Xu, “Multiple sequence alignment using the Hidden Markov Model trained by an improved quantum-behaved particle swarm optimization,” Information Sciences, vol. 182, no. 1, pp. 93–114, 2012.

Digital Library

[52]

L. Huang, M. L. Xi, and Y. H. Zhou, “An improved quantum-behaved particle swarm optimization with random selection of the optimal individual,” in Proceedings of the 2010 WASE International Conference on Information Engineering (ICIE), pp. 189–193, Beidai, China, August 2010.

[53]

Y. Li, R. Xiang, L. Jiao, and R. Liu, “An improved cooperative quantum-behaved particle swarm optimization,” Soft Computing, vol. 16, no. 6, pp. 1061–1069, 2012.

Digital Library

[54]

L. d. S. Coelho, “A quantum particle swarm optimizer with chaotic mutation operator,” Chaos, Solitons & Fractals, vol. 37, no. 5, pp. 1409–1418, 2008.

[55]

W. Fang, J. Sun, H. Chen, and X. Wu, “A decentralized quantum-inspired particle swarm optimization algorithm with cellular structured population,” Information Sciences, vol. 330, pp. 19–48, 2016.

Digital Library

[56]

D. Tang, Y. Cai, J. Zhao, and Y. Xue, “A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems,” Information Sciences, vol. 289, pp. 162–189, 2014.

[57]

D. Tang, S. Dong, X. Cai, and J. Zhao, “A two-stage quantum-behaved particle swarm optimization with skipping search rule and weight to solve continuous optimization problem,” Neural Computing & Applications, vol. 27, no. 8, pp. 2429–2440, 2016.

Digital Library

[58]

T. Liu, L. Jiao, W. Ma, and R. Shang, “Quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems,” Communications in Nonlinear Science and Numerical Simulation, vol. 44, pp. 167–183, 2017.

[59]

S. M. Shu-Mei Guo and C. C. Chin-Chang Yang, “Enhancing differential evolution utilizing eigenvector-based crossover operator,” IEEE Transactions on Evolutionary Computation, vol. 19, no. 1, pp. 31–49, 2015.

Digital Library

[60]

A. S. Lodge, Body Tensor Fields in Continuum Mechanics with Applications to Polymer Theology, Academic Press, Cambridge, MA, USA, 1974.

[61]

W. Chu, X. G. Gao, and S. Sorooshian, “Fortify particle swam optimizer (PSO) with principal components analysis: A case study in improving bound-handling for optimizing high-dimensional and complex problems,” in Proceedings of the 2011 IEEE Congress on Evolutionary Computation, pp. 1644–1648, New Orleans, LA, USA, June 2011.

[62]

A. Kuznetsova, G. Pons-Moll, and B. Rosenhahn, “PCA-enhanced stochastic optimization methods,” in Proceedings of the 2012 Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium, pp. 377–386, Graz, Austria, August 2012, Lecture Notes in Computer Science.

[63]

X. S. Yang and S. Deb, “Cuckoo Search via L´evy Flights,” in Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214, Coimbatore, India, December 2009.

[64]

S. M. Guo and C. C. Yang, “Enhancing differential evolution utilizing eigenvector-based crossover operator,” IEEE Transactions on Evolutionary Computation, vol. 19, no. 1, pp. 31–49, 2014.

[65]

N. H. Awad, M. Z. Ali, P. N. Suganthan, and R. G. Reynolds, “An Ensemble Sinusoidal Parameter Adaptation Incorporated with L-SHADE for Solving CEC2014 Benchmark Problems,” in Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2958–2965, Vancouver, BC, Canada, July 2016.

[66]

J. J. Liang, B. Y. Qu, and P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization,” Computational Intelligence Laboratory, vol. 201212, no. 34, pp. 281–295, 2013, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report.

[67]

J. J. Liang, B. Y. Qu, and P. N. Suganthan, “Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization,” Computational Intelligence Laboratory, vol. 635, p. 490, 2013, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore.

[68]

S. Ali, S. Hassan, and A. Yadav, “A dynamic metaheuristic optimization model inspired by biological nervous systems: neural network algorithm,” Applied Soft Computing, vol. 71, pp. 747–782, 2018.

[69]

A. F. Nematollahi, A. Rahiminejad, and B. Vahidi, “A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization,” Applied Soft Computing, vol. 59, pp. 596–621, 2017.

Digital Library

[70]

T. Liao, D. Aydın, and T. Stützle, “Artificial bee colonies for continuous optimization: experimental analysis and improvements,” Swarm Intelligence, vol. 7, no. 4, pp. 327–356, 2013.

[71]

M. M. Eusuff and K. E. Lansey, “Optimization of water distribution network design using the shuffled frog leaping algorithm,” Journal of Water Resources Planning and Management, vol. 129, no. 3, pp. 210–225, 2003.

[72]

S. Mirjalili, “SCA: a Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, pp. 120–133, 2016.

Digital Library

[73]

P. Civicioglu, “Artificial cooperative search algorithm for numerical optimization problems,” Information Sciences, vol. 229, pp. 58–76, 2013.

Digital Library

[74]

A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 398–417, 2009.

Digital Library

[75]

Z. H. Zhan, J. Zhang, Y. Li, and H. S. Hung Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 6, pp. 1362–1381, 2009.

Digital Library

[76]

J. Derrac, S. García, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3–18, 2011.

[77]

V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, NY, USA, 1995.

Digital Library

[78]

D. Tang, Z. Liu, J. Yang, and J. Zhao, “Memetic frog leaping algorithm for global optimization,” Soft Computing, vol. 23, no. 21, pp. 11077–11105, 2019.

[79]

D. Tang, S. Dong, Y. Jiang, H. Li, and Y. Huang, “ITGO: invasive tumor growth optimization algorithm,” Applied Soft Computing, vol. 36, pp. 670–698, 2015.

Digital Library

[80]

M. Versaci and A. Palumbo, “Magnetorheological Fluids: qualitative comparison between a mixture model in the Extended Irreversible Thermodynamics framework and an Herschel-Bulkley experimental elastoviscoplastic model,” International Journal of Non-linear Mechanics, vol. 118, 2020.

Cited By

Selvakumar KSelvabharathi DPalanisamy RThamizh Thentral T(2023)CO2 Emission-Constrained Short-Term Unit Commitment Problem Using Shuffled Frog Leaping AlgorithmJournal of Electrical and Computer Engineering10.1155/2023/23366892023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2336689

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cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2021, Issue

2021

8452 pages

ISSN:1687-5265

EISSN:1687-5273

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Copyright © 2021 Deyu Tang et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hindawi Limited

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Published: 01 January 2021

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Cited By

Selvakumar KSelvabharathi DPalanisamy RThamizh Thentral T(2023)CO2 Emission-Constrained Short-Term Unit Commitment Problem Using Shuffled Frog Leaping AlgorithmJournal of Electrical and Computer Engineering10.1155/2023/23366892023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/2336689

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