Bases de datos bibliográficas:

Publicaciones del académico:

• Initialization and Diversity in Optimization Algorithms

• Analyzing metaheuristic algorithms performance and the causes of the zero-bias problem: a different perspective in benchmarks
• Diversity Measurement in Different PSO Variants Applied to Global Optimization and Classical Engineering Problems
• Simulating flood situations in urban hydrology using a diffusion model in complex networks
• Addressing Limitations and Inaccuracy of Diversity Metrics in Evolutionary Algorithms with an Accurate Dimension-Wise Diversity

• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• Exploration Paths Derived from Trajectories Extracted from Second-Order System Responses
• Enhancing Anisotropic Diffusion Filtering via Multi-objective Optimization
• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• Utilizing the Moth Swarm Algorithm to Improve Image Contrast
• Introduction to Metaheuristic Methods
• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• A Measure of Diversity for Metaheuristic Algorithms Employing Population-Based Approaches
• Fractional Fuzzy Controller Using Metaheuristic Techniques
• Population of Hyperparametric Solutions for the Design of Metaheuristic Algorithms: An Empirical Analysis of Performance in Particle Swarm Optimization
• Population Control in Metaheuristic Algorithms: Can Fewer Be Better?
• Metaheuristics International Conference
• Adaptability and Efficiency in Population Management: A multi-population CMA-ES Strategy for High-Dimensional Optimization
• Handling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approach
• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• Striving for Optimal Equilibrium in Metaheuristic Algorithms: Is It Attainable?
• Population of Hyperparametric Solutions for the Design of Metaheuristic Algorithms: An Empirical Analysis of Performance in Particle Swarm Optimization
• A Novel Method for Initializing Populations Using the Metropolis–Hastings (MH) Technique
• Metaheuristic Algorithms: New Methods, Evaluation, and Performance Analysis
• Considering radial basis function neural network for effective solution generation in metaheuristic algorithms

• A new method to solve rotated template matching using metaheuristic algorithms and the structural similarity index
• Improving Metaheuristic Algorithm Design Through Inequality and Diversity Analysis: A Novel Multi-Population Differential Evolution
• Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes
• A novel diversity-aware inertia weight and velocity control for particle swarm optimization
• An Hyper-Heuristic Based Population Management Through Statistical Analysis and Phases Optimization
• Improving the Convergence of the PSO Algorithm with a Stagnation Variable and Fuzzy Logic

• Studies in Computational Intelligence
• A Review of the Use of Quasi-random Number Generators to Initialize the Population in Meta-heuristic Algorithms
• An agent-based transmission model of COVID-19 for re-opening policy design
• An improved multi-population whale optimization algorithm
• Improving the optimization performance by an adaptable design: A dynamic selection of operators via criteria-based matrix for evolutionary algorithms
• Handling stagnation through diversity analysis: A new set of operators for evolutionary algorithms
• Solving Reality-Based Trajectory Optimization Problems with Metaheuristic Algorithms Inspired by Metaphors
• A diversity metric for population-based metaheuristic algorithms

• Population management in metaheuristic algorithms: Could less be more?
• Group-based synchronous-asynchronous grey wolf optimizer
• Improving the segmentation of magnetic resonance brain images using the LSHADE optimization algorithm

• A better balance in metaheuristic algorithms: Does it exist? Swarm and Evolutionary Computation, 54, 100671
• An improved clustering method based on biological visual models
• A better balance in metaheuristic algorithms: Does it exist?
• A better balance in metaheuristic algorithms: Does it exist? Swarm Evol Comput 54: 100671
• A better balance in metaheuristic algorithms: Does it exist?, Swarm Evol. Comput., 54 (2020), 100671
• A better balance in metaheuristic algorithms: does it exist? Swarm Evol. Comput. 54, 100671 (2020)