A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature
Nonfiction, Science & Nature, Science, Biological Sciences, Botany, Computers, Advanced Computing, Artificial Intelligence, General Computing
| Author: | Camilo Caraveo, Fevrier Valdez, Oscar Castillo | ISBN: | 9783030055516 |
| Publisher: | Springer International Publishing | Publication: | December 30, 2018 |
| Imprint: | Springer | Language: | English |
| Author: | Camilo Caraveo, Fevrier Valdez, Oscar Castillo |
| ISBN: | 9783030055516 |
| Publisher: | Springer International Publishing |
| Publication: | December 30, 2018 |
| Imprint: | Springer |
| Language: | English |
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.
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.
