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A Visualizable Test Problem Generator for Many-Objective Optimization
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DOI：10.1109/TEVC.2021.3084119.png)
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Abstract
En 中文
Visualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distance-based many-objective optimization test problems have been developed to facilitate visualization of search behavior in a 2-D design space with arbitrarily many objective functions. Previous works have proposed a few commonly seen problem characteristics into this problem framework, such as the definition of disconnected Pareto sets and dominance resistant regions of the design space. The authors' previous work has advanced this research further by providing a problem generator to automatically create user-defined problem instances featuring any combination of these problem features as well as newly introduced ones, such as landscape discontinuities, varying objective ranges, and neutrality. This work makes a number of additional contributions including the proposal of an enhanced, open-source feature-rich problem generator that can create user-defined problem instances exhibiting a range of problem features-some of which are newly introduced here or form extensions of existing features. A comprehensive validation of the problem generator is also provided using popular multiobjective optimization algorithms, and some problem generator settings to create instances exhibiting different challenges for an optimizer are identified.
Keywords:
Generators
Visualization
Optimization
Search problems
Task analysis
Sociology
Shape
Benchmarking
evolutionary optimization
multiobjective test problems
test suite
visualization
Journal
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12
Papers:
1.9K
Citations:
2.4W
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