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

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

This paper provides a survey of the research done on optimization in dynamic environments
over the past decade. We show an analysis of the most commonly used problems, methods …

This work describes a forward-looking approach for the solution of dynamic (time-changing)
problems using evolutionary algorithms. The main idea of the proposed method is to …

Traditional ant colony optimization (ACO) algorithms have difficulty in addressing dynamic
optimization problems (DOPs). This is because once the algorithm converges to a solution …

Many real-world optimization problems are subject to dynamic environments that require an
optimization algorithm to track the optimum during changes. Ant colony optimization (ACO) …

An essential feature of a dynamic multiobjective evolutionary algorithm (MOEA) is to
converge quickly to the Pareto-optimal Set before it changes. In cases where the behavior of …

There is a wide range of publications reported in the literature, considering optimization
problems where the entire problem related data remains stationary throughout optimization …

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 …

Dynamic optimization problems (DOPs) are those whose specifications change over time
during the optimization, resulting in continuously moving optima. Most research work on …