Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima
in Dynamic Environments
- URL: http://arxiv.org/abs/2201.00523v2
- Date: Thu, 6 Jan 2022 09:20:13 GMT
- Title: Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima
in Dynamic Environments
- Authors: Wenjian Luo, Xin Lin, Changhe Li, Shengxiang Yang, Yuhui Shi
- Abstract summary: multimodal optimization problems (DMMOPs) have been studied in the field of evolutionary and swarm intelligence for years.
In this competition, a test suit about DMMOPs is given, which models the real-world applications.
The metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments.
- Score: 15.075191738272098
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Dynamic and multimodal features are two important properties and widely
existed in many real-world optimization problems. The former illustrates that
the objectives and/or constraints of the problems change over time, while the
latter means there is more than one optimal solution (sometimes including the
accepted local solutions) in each environment. The dynamic multimodal
optimization problems (DMMOPs) have both of these characteristics, which have
been studied in the field of evolutionary computation and swarm intelligence
for years, and attract more and more attention. Solving such problems requires
optimization algorithms to simultaneously track multiple optima in the changing
environments. So that the decision makers can pick out one optimal solution in
each environment according to their experiences and preferences, or quickly
turn to other solutions when the current one cannot work well. This is very
helpful for the decision makers, especially when facing changing environments.
In this competition, a test suit about DMMOPs is given, which models the
real-world applications. Specifically, this test suit adopts 8 multimodal
functions and 8 change modes to construct 24 typical dynamic multimodal
optimization problems. Meanwhile, the metric is also given to measure the
algorithm performance, which considers the average number of optimal solutions
found in all environments. This competition will be very helpful to promote the
development of dynamic multimodal optimization algorithms.
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