Abstract:Global optimization of dynamic cost functions is important in many engineering applications. For these tasks, global optima change over time, or are greatly affected by dynamic noise. Nature-based stochastic methods, including genetic algorithms, particle swarm optimization (PSO), and differential evolution, have been particularly effective in dynamic optimization. However, these methods are generally very computationally intensive, and consequently research has focused on parallelization paradigms. In this pa… Show more
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