Abstract
In the optimization of problems in dynamic environments, algorithms need to not only find the global optimal solutions in a specific environment but also to continuously track the moving optimal solutions over dynamic environments. To address this requirement, a species conservation-based particle swarm optimization (PSO), combined with a spatial neighbourhood best searching technique, is proposed. This algorithm employs a species conservation technique to save the found optima distributed in the search space, and these saved optima either transferred into the new population or replaced by the better individual within a certain distance in the subsequent evolution. The particles in the population are attracted by its history best and the optimal solution nearby based on the Euclidean distance other than the index-based. An experimental study is conducted based on the moving peaks benchmark to verify the performance of the proposed algorithm in comparison with several state-of-the-art algorithms widely used in dynamic optimization problems. The experimental results show the effectiveness and efficiency of the proposed algorithm for tracking the moving optima in dynamic environments.
Highlights
As a very challenging optimization tool, evolutionary algorithms (EAs) have been successfully applied to the optimization problems in static environments
Particle swarm optimization algorithms have been widely used in the optimization in static environments, and some promising results have been achieved in recent years when it was applied to address dynamic optimization problems (DOPs)
A species conservation-based particle swarm optimization (PSO) combined with a spatial neighbourhood best searching is proposed for DOPs
Summary
As a very challenging optimization tool, evolutionary algorithms (EAs) have been successfully applied to the optimization problems in static environments. EAs have not been effectively used to solve optimization problems in dynamic environments, which are very common in many real-world applications, for example, the changes of vehicle routing due to the temporary traffic control or sudden changes in weather, the newly added artefacts in production scheduling, and uncertain market factors lead to changes in financial trading models. In order to increase or maintain the population diversity, researchers have developed many schemes to enhance the canonical EAs’ ability to locate moving optimum in dynamic environments. Speciesbased EAs are often regarded as a kind of multiswarm algorithm It preserves the candidate solutions distributed in the search space according to a predefined radius, and after the evolution in each generation, the saved species seeds are either replacing the worse individual, if there has an unprocessed individual within the search radius, or replacing the worst unprocessed individual in the current population, if there is no solution within the search radius [6].
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