A5033809974- Metaheuristic Optimization Algorithms Research
- Poverty, Education, and Child Welfare
- Vehicle Routing Optimization Methods
- Economic Growth and Development
- Banking Sector Performance and Management
- Gender, Education, and Development Issues
- Microfinance and Financial Inclusion
- Evolutionary Algorithms and Applications
- ICT Impact and Policies
- Advanced Multi-Objective Optimization Algorithms
- Optimization and Packing Problems
- Public Policy and Governance
- Optimization and Search Problems
- Educational Outcomes and Influences
- Aging, Elder Care, and Social Issues
- Business, Innovation, and Economy
- Agriculture and Rural Development Research
- Latin American socio-political dynamics
- Advanced Bandit Algorithms Research
- Higher Education and Sustainability
- Machine Learning and ELM
- Higher Education in Latin America
- Educational Robotics and Engineering
- Public Health and Environmental Issues
- Underground infrastructure and sustainability
Pontificia Universidad Católica de Valparaíso
2020-2024
Centro de Innovación Aplicada en Tecnologías Competitivas
2014
International Center for Tropical Agriculture
2014
For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation previous review and seeks to draw comprehensive picture various ways binarize this type metaheuristics; study uses standard systematic consisting analysis 512 publications from 2017 January 2022 (5 years). The work will provide theoretical foundation novice researchers tackling optimization using metaheuristic algorithms expert...
One of the central issues that must be resolved for a metaheuristic optimization process to work well is dilemma balance between exploration and exploitation. The metaheuristics (MH) achieved this can called balanced MH, where Q-Learning (QL) integration framework was proposed selection operators conducive balance, particularly binarization schemes when continuous solves binary combinatorial problems. In use extended other recent metaheuristics, demonstrating QL in improves...
Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend the field order to design approaches successfully solve them. Thus, well-known strategy includes employment of continuous swarm-based algorithms transformed perform binary environments. work, we propose hybrid approach that contains discrete smartly adapted population-based strategies efficiently tackle The proposed employs reinforcement...
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in optimization process, as it can have a significant impact performance algorithm. Different schemes are presented and set actions, which combine different transfer functions rules, under selector based reinforcement learning proposed. The experimental results show that rules greater than algorithms some sets actions statistically better...
In recent years, continuous metaheuristics have been a trend in solving binary-based combinatorial problems due to their good results. However, use this type of metaheuristics, it is necessary adapt them work binary environments, and general, adaptation not trivial. The method proposed evaluates the reinforcement learning techniques binarization process. Specifically, backward Q-learning technique explored choose schemes intelligently. This allows any metaheuristic be adapted environments....
In the different situations present in industry, combinatorial problems are increasingly frequent. This paper presents interaction of Metaheuristics and Machine Learning, specifically as Learning can be a support to enhance Metaheuristics. The resolution Set Covering Problem is presented, using Grey Wolf Optimizer Sine Cosine Algorithm metaheuristics that have been improved by adding Q-Learning technique for selection Discretization Scheme, two-steps, intelligently choosing which transfer...
Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques one these strategies that particularly noteworthy due breadth applications. In this article, a hybrid algorithm is proposed integrates k-means generate binary version cuckoo search technique, strengthened by local operator. The applied NP-hard Set-Union Knapsack Problem. This problem has...
When we face real problems using computational resources, understand that it is common to find combinatorial in binary domains. Moreover, have take into account a large number of possible candidate solutions, since these can be numerous and make complicated for classical algorithmic techniques address them. this happens, most cases, becomes problem due the high resource cost they generate, so utmost importance solve efficiently. To cope with problem, apply other methods, such as...
Currently, the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems. In this sense, metaheuristics have been a common trend field order to design approaches solve them successfully. Thus, well-known strategy consists use of algorithms based on discrete swarms transformed perform binary environments. Following No Free Lunch theorem, we are interested testing performance Fruit Fly Algorithm, bio-inspired metaheuristic for deducing global...
This study focuses on identifying personality traits in computer science students and determining whether they are related to academic performance. In addition, the importance of based motivation scale depression, anxiety, stress scales were measured. A sample 188 from Computer Engineering Schools Pontifical Catholic University Valparaíso was used. Through econometric two-stage least squares paired correlation analysis, results obtained indicate that there is a relation between performance...
In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as action selection mechanism, and applied herein a selector for binarization schemes. These schemes enable continuous metaheuristics to be binary problems, thereby paving new paths combinatorial optimization. To evaluate its efficacy, implemented within our BSS framework, which integrates variety learning metaheuristic techniques. Upon resolving 45 instances Set Covering Problem,...
This paper presents the development of an eco-efficient product/process, which has improved mechanical properties from introduction natural fibres in EPDM (Ethylene-Propylene-Diene-Terpolymer) rubber formulation. The optimisation analysis is made by a fractional factorial design 211-7. Different formulations were evaluated using multi-response desirability function, with aim finding efficient levels for manufacturing time-cycle, improving product, and reducing raw material costs....