A benchmark test suite for evolutionary many-objective optimization
By Ran Cheng, Miqing Li, Ye Tian, Xing-yi Zhang, Shengxiang Yang, Yaochu Jin, Xin Yao. Complex & Intelligent Systems, 2017
432 citations recorded by the source corpus.
In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this paper, we carefully select (or modify) 15 test problems with diverse properties to construct a benchmark test suite, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/0c5a7ff79f3e27fd17bf08a86eaa9689d1d7bb55
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Citations in the corpus
- A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, background): “, those with a large number of local fronts [4].”
- A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, neutral): “Similar studies have been also contributed in [92] and [93], focused on realistic many-objective optimization problems.”