Abstract
In this article, we have proposed a search optimization algorithm based on the natural intelligence of biological plants, which has been modelled using a three tier architecture comprising Plant Growth Simulation Algorithm (PGSA), Evolutionary Learning and Reinforcement Learning in each tier respectively. The method combines the heuristic based PGSA along with Evolutionary Learning with an underlying Reinforcement Learning technique where natural selection is used as a feedback. This enables us to achieve a highly optimized algorithm for search that simulates the evolutionary techniques in nature. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run times of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.
This study was supported by the Brain Korea 21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005) and the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP). [No. 10041145, Self-Organized Software platform (SoSp) for Welfare Devices].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bose, I., Karmakar, R.: Simple models of plant learning and memory. Phys. Scr. 2003(T106), 1–10 (2003)
Hopfield, J.J.: Neural Networks and Physical Systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79, 2554–2558 (1982)
Inoue, J., Chakrabarti, B.K.: Competition between ferro-retrieval and antiferro orders in Hopfield- like network model plant intelligence. Physica A 346, 58–67 (2004)
Tong, L., Zhong-tuo, W.: Application of plant growth simulation algorithm on solving facility location problem. Syst. Eng. Theory Pract. 28(12), 107–115 (2008)
Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer, New York (1990)
Sutton, R.S.: Temporal credit assignment in reinforcement learning. Ph.D. Thesis, Department of Computer and Information Science, University of Massachusetts (1984)
Trewavas, A.J.: Calcium makes waves. Plant Physiol. 120, 1–6 (1999)
Trewavas, A.J.: Green plants as intelligent organisms. Trends Plant Sci. 10(9), 413–419 (2005)
Xu, L., Tao, M., Ming, H.: A hybrid Algorithm based on genetic algorithm and plant growth simulation. In: International Conference on Measurement, Information and Control (MIC), vol. 1, pp. 445–448 (2012)
Jing, Y., Wang Zong F.: A refined plant growth simulation algorithm for distribution network configuration. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 1, pp. 357–361 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Bhattacharjee, D., Paul, A. (2016). A Hybrid Search Optimization Technique Based on Evolutionary Learning in Plants. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-41000-5_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40999-3
Online ISBN: 978-3-319-41000-5
eBook Packages: Computer ScienceComputer Science (R0)