Metaheuristics for Dynamic Optimization pp 35-59

Part of the Studies in Computational Intelligence book series (SCI, volume 433)

Dynamic Function Optimization: The Moving Peaks Benchmark

Abstract

Many practical, real-world applications have dynamic features. If the changes in the fitness function of an optimization problem are moderate, a complete restart of the optimization algorithm may not be warranted. In those cases, it is meaningful to apply optimization algorithms that can accommodate change. In the recent past, many researchers have contributed algorithms suited for dynamic problems. To facilitate the comparison between different approaches, the Moving Peaks (MP) function was devised. This chapter reviews all known optimization algorithms that have been tested on the dynamic MP problem. The majority of these approaches are nature-inspired. The results of the best-performing solutions based on the MP benchmark are directly compared and discussed. In the concluding remarks, the main characteristics of good approaches for dynamic optimization are summarised.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Information & Communication TechnologiesSwinburne University of TechnologyMelbourneAustralia
  2. 2.Faculty of Higher EducationSwinburne University of TechnologyMelbourneAustralia

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