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

Camilo Caraveo

ISBN 10: 3030055507 / ISBN 13: 9783030055509

Published by Springer International Publishing, Springer International Publishing, 2019

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783030055509

Bibliographic Details

• Title: A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 3030055507
• ISBN 13: 9783030055509
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.