Abstract
This paper presents a novel Simple Human Learning Optimization (SHLO) algorithm, which is inspired by human learning mechanisms. Three learning operators are developed to generate new solutions and search for the optima by mimicking the learning behaviors of human. The 0-1 knapsack problems are adopted as benchmark problems to validate the performance of SHLO, and the results are compared with those of binary particle swarm optimization (BPSO), modified binary differential evolution (MBDE), binary fruit fly optimization algorithm (bFOA) and adaptive binary harmony search algorithm (ABHS). The experimental results demonstrate that SHLO significantly outperforms BPSO, MBDE, bFOA and ABHS. Considering the ease of implementation and the excellence of global search ability, SHLO is a promising optimization tool.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Boston (1989)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Proceedings of First European Conference on Artificial Life. MIT Press/Bradford Books, Paris (1991)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Network vol. 4, pp. 1942–1948. IEEE Press (1995)
Lee, K.S., Geem, Z.W.: A New Meta-heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice. Computer Methods in Applied Mechanics and Engineering 194, 3902–3933 (2005)
Pan, W.T.: A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example. Knowledge-Based Systems 26, 69–74 (2012)
Fister, J.I., Yang, X.S., Fister, I.: A Brief Review of Nature-Inspired Algorithms for Optimization. Elektrotehnski Vestnik 80, 1–7 (2013)
Forcheri, P., Molfino, M.T., Quarati, A.: ICT Driven Individual Learning: New Opportunities and Perspectives. Educational Technology & Society 3, 51–61 (2000)
Ickes, W., Gonzalez, R.: “Social” Cognition and Social Cognition: From the Subjective to the Intersubjective. Small Group Research 25, 294–315 (1994)
Ellis, A.P.J., Hollenbeck, J.R., Ilgen, D.R., Porter, C.O.L.H., West, B.J., Moon, H.: Team learning: Collectively connecting the Dots. Journal of Applied Psychology 88, 821–835 (2003)
Andrews, K.M., Delahaye, B.L.: Influences on Knowledge Processes in Organizational Learning: The Psychosocial Filter. Journal of Management Studies 37, 797–810 (2002)
McEvily, S.K., Chakravarthy, B.: The Persistence of Knowledge-based Advantage: An Empirical Test for Product Performance and Technological Knowledge. Strategic Management Journal 23, 285–305 (2002)
El-Maleh, A.H., Sheikh, A.T., Sait, S.M.: Binary Particle Swarm Optimization (BPSO) Based State Assignment for Area Minimization of Sequential Circuits. Applied Soft Computing 13, 4832–4840 (2013)
Wu, C.Y., Tseng, K.Y.: Topology Optimization of Structures Using Modified Binary Differential Evolution. Structural and Multidisciplinary Optimization 42, 939–953 (2010)
Wang, L., Zheng, X., Wang, S.: A Novel Binary Fruit Fly Optimization Algorithm for Solving the Multidimensional Knapsack Problem. Knowledge-Based Systems 48, 17–23 (2013)
Wang, L., Yang, R., Xu, Y.: An Improved Adaptive Binary Harmony Search Algorithm. Information Sciences 232, 58–87 (2013)
Zou, D., Gao, L., Li, S., Wu, J.: Solving 0-1 knapsack problem by a novel global harmony search algorithm. Applied Soft Computing 11, 1556–1564 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Ni, H., Yang, R., Fei, M., Ye, W. (2014). A Simple Human Learning Optimization Algorithm. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-662-45261-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45260-8
Online ISBN: 978-3-662-45261-5
eBook Packages: Computer ScienceComputer Science (R0)