A5085743401- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Vehicle Routing Optimization Methods
- Data Stream Mining Techniques
- Robotic Path Planning Algorithms
- Transportation and Mobility Innovations
- Remote Sensing in Agriculture
- Anomaly Detection Techniques and Applications
- Infrared Target Detection Methodologies
- Advanced Image and Video Retrieval Techniques
- Industrial Vision Systems and Defect Detection
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- solar cell performance optimization
- Remote-Sensing Image Classification
- Photovoltaic System Optimization Techniques
- Solar Radiation and Photovoltaics
- Advanced Image Fusion Techniques
- Artificial Intelligence in Games
- Thermography and Photoacoustic Techniques
Universidad de Guadalajara
2020-2023
Particle swarm optimization (PSO) is essential to evolutionary computation algorithms (ECA). The PSO has some drawbacks as premature convergence and stagnation at local minima. Inertia weight a parameter that controls the global exploration exploitation capability in by determining influence of previous velocity on its current motion. This article proposes using counter verifies times stuck same fitness value. In proposed fuzzy controlled with coefficient (FCPSO), controller designed tune...
Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it shortcomings, such as premature convergence stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global exploration exploitation capability by determining influence previous velocity on its current motion. Therefore, article proposes PSO with Diversity-aware Velocity Control (PSOIVC) to improve performance. The PSOIVC employs...
Population size is an important variable in evolutionary algorithms (EA). Its proper configuration improves the performance of search process not only terms fitness function but also for resources required. This article introduced a population management mechanism that includes different operators. Such operators are designed and applied based on diversity population. In general terms, address problems EA regarding stagnation inefficient use evaluations. As case study, proposed method...
The balance between exploration and exploitation is an important feature in Evolutionary Algorithms (EA). use of different operators permits to explore the search space exploit most prominent regions. This article introduces a dynamic operator selection method that considers criteria at same time. proposed approach uses decision matrix (DyDM) identify which must be used each iteration based on how algorithm behaves. DyDM specific information as diversity avoid stagnation, actual work...
Hyper-heuristics (HH) are strategies that have a high-level mechanism to combine, select or generate heuristics at low level find solutions based on the information received during search process. Typically, an HH approach involves evaluating several algorithms and constructing priori structure containing required select, combine appropriate heuristic solve given problem using usually probabilistic selection method. This paper considers constructive online learning with agent population...
In evolutionary algorithms and metaheuristics, defining when applying a specific operator is important. Besides, in complex optimization problems, multiple populations can be used to explore the search space simultaneously. However, one of main problems extracting information from using it evolve solutions. This article presents inequality-based multi-population differential evo-lution (IMDE). algorithm uses K-means generate subpopulations (settlements). Two variables are extracted...