Optimization in dynamic environments is a challenging but important task since many real-
world optimization problems are changing over time. Evolutionary computation and swarm …

In the last decade, bio-inspired self-organising mechanisms have been applied to different
domains, achieving results beyond traditional approaches. However, researchers usually …

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm
optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish …

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted
power supply deployed in a specific area to gather environmental data. One of the most …

The multi-population method has been widely used to solve unconstrained continuous
dynamic optimization problems with the aim of maintaining multiple populations on different …

Many real-world problems are computationally costly and the objective functions evolve over
time. Data-driven, aka surrogate-assisted, evolutionary optimization has been recognized as …

We propose a new particle swarm optimization algorithm for problems where objective
functions are subject to zero-mean, independent, and identically distributed stochastic noise …

Abstract Particle Swarm Optimization (PSO) is a metaheuristic whose performance
deteriorates significantly when utilized on optimization problems subject to noise. On these …

The rolling force model is the core of rolling model, and the prediction accuracy of the Zr-4
alloy cold rolling force model directly affects the control accuracy of the thickness and shape …

We are increasingly seeing computer systems which are expected to function without
operator intervention. This is perhaps acceptable for running computer networks or traffic …