In the real world, many optimization problems are dynamic. This requires an optimization
algorithm to not only find the global optimal solution under a specific environment but also to …

In recent years the genetic algorithm community has shown a growing interest in studying
dynamic optimization problems. Several approaches have been devised. The random …

In recent years, the static shortest path (SP) problem has been well addressed using
intelligent optimization techniques, eg, artificial neural networks, genetic algorithms (GAs) …

To solve dynamic optimization problems, multiple population methods are used to enhance
the population diversity for an algorithm with the aim of maintaining multiple populations in …

In recent years, there has been a growing interest in the study of particle swarm optimization
(PSO) in dynamic environments. This paper presents a new PSO model, called PSO with …

This work proposes a new approach, based on Genetic Algorithms and Support Vector
Machine to trade in the forex market. In this work, a new algorithm capable of generating …

Evolutionary computation is a class of problem optimization methodology with the inspiration
from the natural evolution of species. In nature, the population of a species evolves by …

Many real-world optimization problems are subject to dynamic environments, where
changes may occur over time regarding optimization objectives, decision variables, and/or …

Raw ore allocation in mineral processing is crucial for improving the utilization ratio of
nonrenewable raw mineral resources. Allocation of raw ore is a nonlinear, multiobjective …

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