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Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
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 …
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 …
Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems
In this paper, a novel and powerful metaheuristic optimizer, named the growth optimizer
(GO), is proposed. Its main design inspiration originates from the learning and reflection …
(GO), is proposed. Its main design inspiration originates from the learning and reflection …
Evolutionary algorithms and their applications to engineering problems
The main focus of this paper is on the family of evolutionary algorithms and their real-life
applications. We present the following algorithms: genetic algorithms, genetic programming …
applications. We present the following algorithms: genetic algorithms, genetic programming …
EGNN: Graph structure learning based on evolutionary computation helps more in graph neural networks
In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
fields due to their characteristics of neighborhood aggregation and have achieved state-of …
Evolution of heuristics: Towards efficient automatic algorithm design using large language model
Heuristics are widely used for dealing with complex search and optimization problems.
However, manual design of heuristics can be often very labour extensive and requires rich …
However, manual design of heuristics can be often very labour extensive and requires rich …
Particle swarm optimization or differential evolution—A comparison
In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm
Optimization and Differential Evolution. Their initial versions were very simple, but rapidly …
Optimization and Differential Evolution. Their initial versions were very simple, but rapidly …
Direct fit to nature: an evolutionary perspective on biological and artificial neural networks
Evolution is a blind fitting process by which organisms become adapted to their
environment. Does the brain use similar brute-force fitting processes to learn how to …
environment. Does the brain use similar brute-force fitting processes to learn how to …
Optimized flocking of autonomous drones in confined environments
We address a fundamental issue of collective motion of aerial robots: how to ensure that
large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous …
large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous …