A review of population-based metaheuristics for large-scale black-box global optimization—Part I
Scalability of optimization algorithms is a major challenge in co** with the ever-growing
size of optimization problems in a wide range of application areas from high-dimensional …
size of optimization problems in a wide range of application areas from high-dimensional …
A review of population-based metaheuristics for large-scale black-box global optimization—Part II
This article is the second part of a two-part survey series on large-scale global optimization.
The first part covered two major algorithmic approaches to large-scale optimization, namely …
The first part covered two major algorithmic approaches to large-scale optimization, namely …
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 …
Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms
People can use credit cards for online transactions as it provides an efficient and easy-to-
use facility. With the increase in usage of credit cards, the capacity of credit card misuse has …
use facility. With the increase in usage of credit cards, the capacity of credit card misuse has …
SF-FWA: A Self-Adaptive Fast Fireworks Algorithm for effective large-scale optimization
M Chen, Y Tan - Swarm and Evolutionary Computation, 2023 - Elsevier
Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …
increasingly important in recent years due to the growing complexity of engineering and …
Bio-inspired computation: Where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
with the mimicking of behavioral patterns and social phenomena observed in nature towards …
[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 …
Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization
Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-
objective optimization, where a number of variation operators have been developed to …
objective optimization, where a number of variation operators have been developed to …
Gene targeting differential evolution: a simple and efficient method for large-scale optimization
Large-scale optimization problems (LSOPs) are challenging because the algorithm is
difficult in balancing too many dimensions and in esca** from trapped bottleneck …
difficult in balancing too many dimensions and in esca** from trapped bottleneck …
An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization
As a form of evolutionary computation, particle swarm optimization is less effective in large
scale optimization since it is unable to effectively balance exploration and exploitation. To …
scale optimization since it is unable to effectively balance exploration and exploitation. To …