Chapter 3 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 com-plete 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 al-gorithms 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 re-marks, the main characteristics of good approaches for dynamic optimization are summarised. Dynamic function optimization is an active research area. Over the years, many different algorithms have been proposed to solve various types of dynamic opti