Memetic frog leaping algorithm for global optimization
Developing an effective memetic algorithm that integrates leaning units and achieves the synergistic coordination between exploration and exploitation is a difficult task. In this paper, we propose a memetic algorithm based on the shuffled frog leaping algorithm, which is fulfilled by three units: memetic diffusion component, memetic evolutionary component and memetic learning component. Memetic diffusion component enhances the diversity of population by the shuffled process. Memetic evolutionary component accomplishes the exploitation task by integrating the frog leaping rule, geometric center, Newton’s gravitational force-based gravitational center and Lévy flight operator. Memetic learning component improves the exploration by an adaptive learning rule based on the individual selection and the dimension selection. In order to evaluate the effectiveness of the proposed algorithm, 30 benchmark functions and a real-world optimization problem are used to compare our algorithm against 13 well-known heuristic methods. The experimental results demonstrate that the performance of our algorithm is better than others for the continuous optimization problems.
KeywordsMemetic algorithm Shuffled frog leaping algorithm Gravity search algorithm Lévy flight Continuous optimization
The authors would like to thank the reviewers and editor for their very useful and constructive comments that helped to improve the quality of the paper. This work is supported by the Guang Dong Provincial Natural fund project, Drug-target interaction prediction method based on collaborative intelligent optimization (2016A030310300); the Natural Science Foundation of China under Grant (No. 61501128); NSFC, Research on reasoning of behavior trust for resisting collusive reputation attack (71401045); Guangdong province precise medicine and big data engineering technology research center for traditional Chinese medicine, Guang Dong Provincial Natural fund (2014A030313585, 2015A030310267, 2015A030310483). Major scientific research projects of Guangdong, Research of Behavioral Trust resisting collusion reputation attack based on implicit and explicit big behavior data analysis (2017WTSCX021). Philosophy and Social Sciences of Guangzhou ‘13th Five-Year’ program (2018GZGJ48).
Compliance with ethical standards
Conflict of interest
The author declares that there is no conflict of interest.
The work of this article does not involve use of human participants or animals.
- Bo J, Yuchun T, Yang-Qing Z, Chung-Dar L, Weber I (2005) Support vector machine with the fuzzy hybrid kernel for protein subcellular localization classification. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ’05), Reno, NV, pp 420–423Google Scholar
- Cheng C, Zhang Y, Song M, Cheng G, Guo D, Cao J, Bao X (2014) Quantum-inspired shuffled frog leaping algorithm for spectrum sensing in cooperative cognitive radio network. In: International conference on human centered computing. Springer International Publishing, pp 80–92Google Scholar
- Dawkins R (1976) The selfish gene. Clarendon Press, OxfordGoogle Scholar
- Holliday D, Resnick R, Walker J (1993) Fundamentals of physics. Wiley, New YorkGoogle Scholar
- Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, technical report TR06, Erciyes UniversityGoogle Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceeding IEEE international conference neural network, Perth, Western Australia, pp 1942–1948Google Scholar
- Kóczy LT, Földesi P, Tü˝u-Szabó B (2017) Inf Sci 000: 1–12Google Scholar
- Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Comput Intell Lab, Zhengzhou Univ., Zhengzhou, China, and Nanyang Technol. Univ., Singapore, Tech. Rep. 201311, DecGoogle Scholar
- Liu H, Yi F, Yang H (2016) Adaptive grouping cloud model shuffled frog leaping algorithm for solving continuous optimization problems. Comput Intell Neuro 2016:25Google Scholar
- Luo X-H, Ye Y, Xia L (2008) Solving TSP with shuffled frog-leaping algorithm. In: Eighth international conference on intelligent system design and application, ISDA’08, 3Google Scholar
- Merz CJ, Blake CL (2015) UCI repository of machine learning databases. http://www.ics.uci.edu/-mlearn/MLRepository.html
- Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms, technical reports. 826, Caltech concurrent computation programGoogle Scholar
- Moscato P, Norman M (1989) A competitive and cooperative approach to complex combinatorial search, technical reports. 790, Caltech Concurrent Computation ProgramGoogle Scholar
- Müller CL, Baumgartner B, Sbalzarini IF (2009) Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes. In: Proceedings of IEEE congress evolutionary computation, pp 18–21Google Scholar
- Narimani MR (2011) A new modified shuffle frog leaping algorithm for non-smooth economic dispatch. World Appl Sci J 12(6):803–814Google Scholar
- Omran MGH, Engelbrecht AP, Salman A (2007) Differential evolution based particle swarm optimization. In: Proceedings of swarm intelligence symposium, Honolulu, HI, USA, pp 112–119Google Scholar
- Ou Y, Sun Y (2011) Grid task scheduling strategy based on improved shuffled frog leaping algorithm. Comput Eng 37(21):146–151Google Scholar
- Vasan P (2014) Handbook of research on artificial intelligence techniques and algorithms (2 volumes). https://doi.org/10.4018/978-1-4666-7258-1
- Vasant P, Weber G-W, Dieu VN (2016) Handbook of research on modern optimization algorithms and applications in engineering and economics. https://doi.org/10.4018/978-1-4666-9644-0
- Yang XS, Deb S (2009) Cuckoo search via L´evy flights. Proceedings of world congress on nature and biologically inspired comput IEEE Publications, USA, pp 210–214Google Scholar
- Zhou HG, Yang CD (2006) Using immune algorithm to optimize anomaly detection based on SVM. In: Proceedings of IEEE international machine learning and cybernetics conference, Dalian, China, pp 4257–4261Google Scholar