Abstract
In recent years dynamic optimization problems have attracted a growing interest from the community of stochastic optimization researchers with several approaches developed to address these problems. The goal of this chapter is to present the different tools and benchmarks to evaluate the performances of the proposed algorithms. Indeed, testing and comparing the performances of a new algorithm to the different competing algorithms is an important and hard step in the development process. The existence of benchmarks facilitates this step, however, the success of these benchmarks is conditioned by their use by the community. In this chapter, we cite many tested problems (we focused only on the continuous case), and we only present the most used: the moving peaks benchmark , and the last proposed: the generalized approach to construct benchmark problems for dynamic optimization (also called benchmark GDBG).
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Nakib, A., Siarry, P. (2013). Performance Analysis of Dynamic Optimization Algorithms. In: Alba, E., Nakib, A., Siarry, P. (eds) Metaheuristics for Dynamic Optimization. Studies in Computational Intelligence, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30665-5_1
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DOI: https://doi.org/10.1007/978-3-642-30665-5_1
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