Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
As the world moves towards industrialization, optimization problems become more
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms …
A survey on evolutionary computation for complex continuous optimization
Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …
development of the economy and society. Moreover, the technologies like Internet of things …
Evolutionary large-scale multi-objective optimization: A survey
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …
solving various optimization problems, but their performance may deteriorate drastically …
A survey on evolutionary neural architecture search
Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …
architectures of DNNs play a crucial role in their performance, which is usually manually …
Multi-strategy competitive-cooperative co-evolutionary algorithm and its application
X Zhou, X Cai, H Zhang, Z Zhang, T **, H Chen… - Information …, 2023 - Elsevier
In order to effectively solve multi-objective optimization problems (MOPs) and fully balance
uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary …
uniformity and convergence, a multi-strategy competitive-cooperative co-evolutionary …
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 …
A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling
The flexible job shop scheduling problem (FJSP) is significant for realistic manufacturing.
However, the job processing time usually is uncertain and changeable during …
However, the job processing time usually is uncertain and changeable during …
A mayfly optimization algorithm
This paper introduces a new method called the Mayfly Algorithm (MA) to solve optimization
problems. Inspired from the flight behavior and the mating process of mayflies, the proposed …
problems. Inspired from the flight behavior and the mating process of mayflies, the proposed …
A comprehensive review on multi-objective optimization techniques: Past, present and future
Realistic problems typically have many conflicting objectives. Therefore, it is instinctive to
look at the engineering problems as multi-objective optimization problems. This paper briefly …
look at the engineering problems as multi-objective optimization problems. This paper briefly …