A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems
In the last few years, the formulation of real-world optimization problems and their efficient
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …
solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In …
Pymoo: Multi-objective optimization in python
Python has become the programming language of choice for research and industry projects
related to data science, machine learning, and deep learning. Since optimization is an …
related to data science, machine learning, and deep learning. Since optimization is an …
[HTML][HTML] On generating trustworthy counterfactual explanations
Deep learning models like chatGPT exemplify AI success but necessitate a deeper
understanding of trust in critical sectors. Trust can be achieved using counterfactual …
understanding of trust in critical sectors. Trust can be achieved using counterfactual …
[HTML][HTML] A prescription of methodological guidelines for comparing bio-inspired optimization algorithms
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …
growing research topic with many competitive bio-inspired algorithms being proposed every …
Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations
In recent algorithmic family simulates different biological processes observed in Nature in
order to efficiently address complex optimization problems. In the last years the number of …
order to efficiently address complex optimization problems. In the last years the number of …
[PDF][PDF] Python parallel processing and multiprocessing: A rivew
Parallel and multiprocessing algorithms break down significant numerical problems into
smaller subtasks, reducing the total computing time on multiprocessor and multicore …
smaller subtasks, reducing the total computing time on multiprocessor and multicore …
Nature inspired optimization algorithms or simply variations of metaheuristics?
In the last decade, we observe an increasing number of nature-inspired optimization
algorithms, with authors often claiming their novelty and their capabilities of acting as …
algorithms, with authors often claiming their novelty and their capabilities of acting as …
State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression
Abstract State-of-Health (SOH) estimation is crucial for the safety and reliability of battery-
based applications. Data-driven methods have shown their promising potential in battery …
based applications. Data-driven methods have shown their promising potential in battery …
Machine learning based surrogate models for microchannel heat sink optimization
Microchannel heat sinks are an efficient cooling method for semiconductor packages.
However, to properly cool increasingly complex and thermally dense circuits, microchannel …
However, to properly cool increasingly complex and thermally dense circuits, microchannel …
An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints
Z Zhu, X Zhou - Computers & Industrial Engineering, 2020 - Elsevier
Concentrated on the production scheduling of complex products that are assembled by
multiple and multilevel manufactured parts, this paper studies the flexible job shop …
multiple and multilevel manufactured parts, this paper studies the flexible job shop …