HOME View All Genre

Book

Audiobook

GENRES 🌙 Dark

Initialization and Diversity in Optimization Algorithms - Diego Oliva, Marco Antonio Perez Cisneros, Bernardo Morales-Castañeda & Mario A. Navarro Velázquez

By Diego Oliva, Marco Antonio Perez Cisneros, Bernardo Morales-Castañeda & Mario A. Navarro Velázquez

Release Date: 2026-02-19

Genre: Mathematics

(0 ratings)
Read Download
Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.

Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.

This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains how it can be incorporated as a mechanism for deciding when to apply operators during the optimization process. Multiple examples are provided in the book using two classical algorithms: Differential Evolution and Particle Swarm Optimization. It includes MATLAB® code and offers several exercises that readers can use for experimentation and design purposes.

Initialization and Diversity in Optimization Algorithms - Diego Oliva, Marco Antonio Perez Cisneros, Bernardo Morales-Castañeda & Mario A. Navarro Velázquez

By Diego Oliva, Marco Antonio Perez Cisneros, Bernardo Morales-Castañeda & Mario A. Navarro Velázquez

Release Date: 2026-02-19

Genre: Mathematics

(0 ratings)
Read Download
Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.

Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.

This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains how it can be incorporated as a mechanism for deciding when to apply operators during the optimization process. Multiple examples are provided in the book using two classical algorithms: Differential Evolution and Particle Swarm Optimization. It includes MATLAB® code and offers several exercises that readers can use for experimentation and design purposes.

More by Diego Oliva, Marco Antonio Perez Cisneros, Bernardo Morales-Castañeda & Mario A. Navarro Velázquez

Advances and Applications of Optimised Algorithms in Image Processing

Artificial Intelligence for COVID-19

Engineering Applications of Modern Metaheuristics

Technological and Industrial Applications Associated With Industry 4.0

Applications of Hybrid Metaheuristic Algorithms for Image Processing

Modern Metaheuristics in Image Processing

Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems

Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)

Evolutionary Computation Techniques: A Comparative Perspective

Artificial Intelligence Using Federated Learning

Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions

Technological and Industrial Applications Associated with Intelligent Logistics

Differential Evolution: From Theory to Practice

Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

Initialization and Diversity in Optimization Algorithms

Metaheuristic Algorithms for Image Segmentation: Theory and Applications

Metaheuristics in Machine Learning: Theory and Applications

Related Articles