Authors:
- Presents a new meta-heuristic optimization algorithm, inspired by plant self-defense mechanisms in nature
- Based on the predator–prey mathematical model by Lotka and Volterra
- Written by experts in the field
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
This book presents a new meta-heuristic algorithm, inspired by the self-defense mechanisms of plants in nature. Numerous published works have demonstrated the various self-defense mechanisms (survival strategies) plants use to protect themselves against predatory organisms, such as herbivorous insects. The proposed algorithm is based on the predator–prey mathematical model originally proposed by Lotka and Volterra, consisting of two nonlinear first-order differential equations, which allow the growth of two interacting populations (prey and predator) to be modeled. The proposed meta-heuristic is able to produce excellent results in several sets of benchmark optimization problems. Further, fuzzy logic is used for dynamic parameter adaptation in the algorithm.
Authors and Affiliations
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Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico
Camilo Caraveo, Fevrier Valdez, Oscar Castillo
Bibliographic Information
Book Title: A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature
Authors: Camilo Caraveo, Fevrier Valdez, Oscar Castillo
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-030-05551-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-05550-9Published: 14 January 2019
eBook ISBN: 978-3-030-05551-6Published: 30 December 2018
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: VIII, 57
Topics: Computational Intelligence, Artificial Intelligence, Plant Sciences, Optimization