# A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems

### Abstract

Optimization technology is used to accelerate decision-making processes and to increase the quality of decision making in management and engineering problems. The development technology has made real-world problems large and complex. Many optimization methods that proposed for solving LSGO problems suffer from the “curse of dimensionality”, which implies that their performance deteriorates quickly as the dimensionality of the search space increases. Therefore, more efficient and robust algorithms are needed. When literature on large-scale optimization problems is examined, it is seen that algorithms with effective global search ability have better results. For the purpose, in this paper, Modified Artificial Algae Algorithm (MAAA) is proposed by modifying the original version of Artificial Algae Algorithm (AAA) inspiring by Differential Evolution Algorithm’s mutation strategies. AAA and MAAA are compared with each other by operating with the first 10 benchmark functions of CEC2010 Special Session on Large Scale Global Optimization. The results show that the hybridization process that applied by updating an additional fourth dimension with mutation strategies of DE after the helical motion of the AAA algorithm, contributes exploration phase and improves the AAA performance on LSGO.### Downloads

### References

R. E. Bellman, Dynamic Programming, Princeton University Press, Princeton, 1957.

Y. Sun, X. Wang, Y. Chen and Z. Liu,” A modified whale optimization algorithm for large-scale global optimization problems”, Expert Systems with Applications, 2018.

W. Long, J. Jiao, X. Liang and M. Tang,” Inspired grey wolf optimizer for solving large-scale function optimization problems”, Applied Mathematical Modelling, 2018, pp. 112-126.

A. F. Ali, M. A. Tawhid, “A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems”, Ain Shams Engineering Journal, 2017.

N. Noman and H. Iba, “Enhancing differential evolution performance with local search for high dimensional function optimization”, In Proceedings of the 7th annual conference on Genetic and evolutionary computation, 2005, pp. 967-974.

D. Molina, M. Lozano and F. Herrera,”MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization”, In Evolutionary Computation (CEC), 2010, pp. 1-8.

Z. Yang, K. Tang, and X. Yao,” Large scale evolutionary optimization using cooperative coevolution”. Information Sciences, 2008, pp. 2985-2999.

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

S.A. Uymaz, G. Tezel and E. Yel, “Artificial algae algorithm with multi-light source for numerical optimization and applications”, Biosystems, 2015b, pp. 25-38.

A.Babalik, A. Ozkis, S.A Uymaz and M.S. Kiran, ”A multi-objective artificial algae algorithm”, Applied Soft Computing, 2018, pp. 377-395.

X. Zhang, C. Wu, J. Li, X. Wang, Z. Yang, J. M. Lee and K. H. Jung, ”Binary artificial algae algorithm for multidimensional knapsack problems”, Applied Soft Computing, 2016, 43, pp. 583-595.

M. Kumar and J. S. Dhillon, “Hybrid artificial algae algorithm for economic load dispatch”, Applied Soft Computing, 2018, 71, pp.89-109.

M. Beşkirli, İ. Koç, H. Haklı and H. Kodaz,” A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm”, Renewable Energy, 2018, 121, pp. 301-308.

R. Storn and K. V. Price, “Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces,” ICSI, USA, Tech. Rep. TR-95-012, 1995 [Online]. Available:

http://www1.icsi.berkeley.edu/~storn/TR-95-012.pdf

Y. Gao and Y. Wang, “A memetic differential evolutionary algorithm

for high dimensional function spaces optimization”, in Proc. 3rd ICNC

, 2007, pp. 188–192.

Z. Yang, K. Tang, and X. Yao, “Differential evolution for high dimensional function optimization,” in Proc. IEEE Congr. Evol. Comput., 2007, pp. 3523–3530.

J. Brest, A. Zamuda, B. Boskovic, M. S. Maucec and V. Zumer, “High dimensional real-parameter optimization using self-adaptive Differential evolution algorithm with population size reduction,” in Proc. IEEE Congr. Evol. Comput., 2008, pp. 2032–2039

K. Tang, X. Yao, P. N. Suganthan, C. MacNish, Y. P. Chen, C. M. Chen, and Z. Yang, “Benchmark functions for the CEC’2008 special session and competition on large scale global optimization”, Nature Inspired Comput. Applicat. Lab., USTC, China, Nanyang Technol. Univ., Singapore, Tech. Rep., 2007

T. Keskintürk, “Diferansiyel Gelişim Algoritması”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi Yıl: 5 Sayı: 9 Bahar 2006, pp.85-99

S. Das and P. N. Suganthan,”Differential evolution: a survey of the state-of-the-art”. IEEE transactions on evolutionary computation, 2011, pp 4-31.

W. Gong, Á. Fialho, Z. Cai and H. Li,” Adaptive strategy selection in differential evolution for numerical optimization: an empirical study”, Information Sciences, 2011, 181(24), pp. 5364-5386.

A. Banitalebi, M. I. A. Aziz and Z. A. Aziz,” A self-adaptive binary differential evolution algorithm for large scale binary optimization problems”, Information Sciences, 2016, 367, pp.487-511.

H. C. Lund and J. W. G. Lund, “Freshwater Algae-Their micoscopic world explored”, Biopress Limited, Bristol, England, 1996.

http://aaa.selcuk.edu.tr/, Accessed on: October 8, 2018.

K. Tang, X. Li, P. N. Suganthan, Z. Yang, T. Weise,” Benchmark functions for the CEC’2010 special session and competition on large scale global”, Nature Inspired Comput. Applicat. Lab., Tech. Rep., 2009

H. Wang, Z. Wu, S. Rahnamayan and D. Jiang,” Sequential DE enhanced by neighborhood search for large scale global optimization”, in Proc. IEEE Congr. Evol. Comput., 2010, pp. 1-7.

H. Wang, H. Sun, C. Li and S. Rahnamayan and J.S. Pan,” Diversity enhanced particle swarm optimization with neighborhood search” Information Sciences, 2013, pp. 119-135.

P. Korošec, K. Tashkova and J. Šilc,” The differential ant-stigmergy algorithm for large-scale global optimization”, in Proc. IEEE Congr. Evol. Comput, 2010, pp. 1-8.

S. Chen, ”Locust Swarms for Large Scale Global Optimization of Nonseparable Problems”, Kukkonen, Benchmarking the Classic Differential Evolution Algorithm on Large-Scale Global Optimization Google Scholar.

*IJISAE*, vol. 6, no. 4, pp. 306-310, Dec. 2018.

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