Mycorrhiza Optimization Algorithm (SpringerBriefs in Applied Sciences and Technology)

✍ Scribed by Fevrier Valdez, Hector Carreon-Ortiz, Oscar Castillo

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 84
• Category: Library

✦ Synopsis

This book provides two new optimization algorithms to address real optimization problems. Optimization is a fundamental concept in engineering and science, and its applications are needed in many fields. From designing products and systems to developing algorithms and models, optimization plays a critical role in achieving efficient and effective solutions to complex problems. Optimization algorithms inspired by nature have proven effective in solving a wide range of problems, including those in engineering, finance, and machine learning. These algorithms are often used when traditional optimization techniques are impractical due to the size or complexity of the problem. In this book, we are presenting two new optimization algorithms inspired by plant roots and the Mycorrhiza Network. The first algorithm is called the Continuous Mycorrhiza Optimization Algorithm (CMOA), which was proposed based on the model of the Continuous Lotka-Volterra System Equations. The second algorithm is called the Discrete Mycorrhiza Optimization Algorithm (DMOA), which design based on the model of Discrete Lotka-Volterra System Equations. By mastering the proposed algorithms, the readers able to develop innovative solutions that improve efficiency, reduce costs, and improve performance in the corresponding field of work.

✦ Table of Contents

Preface
Contents
1 Introduction to the Mycorrhiza Optimization Algorithm
References
2 Theory for the Mycorrhiza Optimization Algorithm
2.1 Optimization
2.2 Mycorrhiza Networks Structure and Function
2.3 Characteristics of Mycorrhized Plants
2.4 The Mother Trees
2.5 Wood Wide Web
References
3 Background for the Mycorrhiza Optimization Algorithm
References
4 Problem Definition for Mycorrhiza Algorithm
References
5 CMOA—Continuous Mycorrhiza Optimization Algorithm
5.1 CMOA Flowchart
5.2 CMOA Pseudocode
5.3 Continuous Lotka-Volterra System Equations
5.4 CMOA Algorithm Parameters
5.5 Benchmark Functions
5.6 Case Studies
5.6.1 CMOA Predator–Prey Model
5.6.2 CMOA Cooperative Model
5.6.3 CMOA Competitive Model
5.6.4 CMOA Ecosystem Model
5.6.5 Results
5.6.6 Hypothesis Test
5.6.7 Comparative CMOA with Others Methods
5.6.8 Results
5.6.9 Behavior
5.6.10 Convergence
References
6 DMOA—Discrete Mycorrhiza Optimization Algorithm
6.1 DMOA Flowchart
6.2 DMOA Pseudocode
6.3 Discrete Lotka-Volterra System Equations
6.4 DMOA Algorithm Parameters
6.5 Case Studies
6.5.1 DMOA Ecosystem Model
6.5.2 Results
6.5.3 Comparative DMOA with Other Methods
6.5.4 Results
6.5.5 Hypothesis Test
6.5.6 Comparative CMOA with DMOA
6.5.7 Behavior Comparative with Other Methods
6.5.8 Behavior Comparative CMOA Versus DMOA
6.5.9 Convergence Comparative CMOA Versus DMOA
References
7 Conclusions of the Mycorrhiza Optimization Algorithm
Index