[HTML][HTML] A review of metaheuristic algorithms for solving TSP-based scheduling optimization problems
Activity-based scheduling optimization is a combinatorial problem built on the traveling
salesman problem intending to optimize people schedules considering their trips and the …
salesman problem intending to optimize people schedules considering their trips and the …
Metaheuristic-based ensemble learning: an extensive review of methods and applications
Ensemble learning has become a cornerstone in various classification and regression tasks,
leveraging its robust learning capacity across disciplines. However, the computational time …
leveraging its robust learning capacity across disciplines. However, the computational time …
[HTML][HTML] Designing problem-specific operators for solving the Cell Switch-Off problem in ultra-dense 5G networks with hybrid MOEAs
The massive deployment of base stations is one of the key pillars of the fifth generation (5G)
of mobile communications. However, this network densification entails high energy …
of mobile communications. However, this network densification entails high energy …
Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19
The novel coronavirus pneumonia (COVID-19) has created huge demands for medical
masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of …
masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of …
[HTML][HTML] Exploiting variability in the design of genetic algorithms to generate telerehabilitation activities
The increasing number of people with impairments and the lack of specialists has led to a
loss of efficiency to deliver proper treatments from National healthcare systems. In this light …
loss of efficiency to deliver proper treatments from National healthcare systems. In this light …
Populating cellular metamaterials on the extrema of attainable elasticity through neuroevolution
The trade-offs between different mechanical properties of materials pose fundamental
challenges in engineering material design, such as balancing stiffness versus toughness …
challenges in engineering material design, such as balancing stiffness versus toughness …
Scheduling optimization of electric ready mixed concrete vehicles using an improved model-based reinforcement learning
The decarbonization of the construction industry has emerged as a pressing global priority,
and the electric machine is the driving force towards achieving this goal. This study focuses …
and the electric machine is the driving force towards achieving this goal. This study focuses …
[HTML][HTML] A metaheuristic-based method for photovoltaic temperature computation under tropical conditions
Tropical climates have favorable irradiation levels for the development of photovoltaic
systems; however, high temperatures have a negative impact on the efficiency of solar cells …
systems; however, high temperatures have a negative impact on the efficiency of solar cells …
The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways
Evolutionary and bioinspired computation are crucial for efficiently addressing complex
optimization problems across diverse application domains. By mimicking processes …
optimization problems across diverse application domains. By mimicking processes …
An efficient deep self-learning artificial orca algorithm for solving ambulance dispatching and calls covering problem
In this paper, a novel Deep Self-Learning Approach applied to Artificial Orca Algorithm and
based on mutation operators is proposed. The idea of contribution comes back to the …
based on mutation operators is proposed. The idea of contribution comes back to the …