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SpringerBriefs in Computational Intelligence

A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature

Authors: Caraveo, Camilo, Valdez, Fevrier, Castillo, Oscar

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  • 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
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  • ISBN 978-3-030-05551-6
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Softcover $69.99
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  • ISBN 978-3-030-05550-9
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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.

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook $54.99
price for USA in USD (gross)
  • ISBN 978-3-030-05551-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA in USD
  • ISBN 978-3-030-05550-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature
Authors
Series Title
SpringerBriefs in Computational Intelligence
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-05551-6
DOI
10.1007/978-3-030-05551-6
Softcover ISBN
978-3-030-05550-9
Series ISSN
2625-3704
Edition Number
1
Number of Pages
VIII, 57
Topics