Abstract
Differential evolution (DE) is a powerful and simple algorithm for single- and multi-objective optimization. However, its performance is highly dependent on the right choice of parameters. To mitigate this problem, mechanisms have been developed to automatically control the parameters during the algorithm run. These mechanisms are usually a part of a unified DE algorithm, which makes it difficult to compare them in isolation. In this paper, we go through various deterministic, adaptive, and self-adaptive approaches to parameter control, isolate the underlying mechanisms, and apply them to a single, simple differential evolution algorithm. We observe its performance and behavior on a set of benchmark problems. We find that even the simplest mechanisms can compete with parameter values found by exhaustive grid search. We also notice that self-adaptive mechanisms seem to perform better on problems which can be optimized with a very limited set of parameters. Yet, adaptive mechanisms seem to behave in a problem-independent way, detrimental to their performance.
M. Drozdik—The work of Martin Drozdik has been supported by the Ministry of Education, Culture, Sports, Science, and Technology of Japan.
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Notes
- 1.
Details on this methodology can be found in [4].
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Drozdik, M., Aguirre, H., Akimoto, Y., Tanaka, K. (2015). Comparison of Parameter Control Mechanisms in Multi-objective Differential Evolution. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_8
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