Abstract If Optuna’s built-in NSGAII has a study obtained from another sampler, but continues with that study, it cannot be used as the first generation, and optimization starts from zero. This means that even if you already know good individuals, you cannot use it in the GA.
In this implementation, the already sampled results are included in the initial individuals of the GA to perform the optimization.
Note, however, that this has the effect that the implementation does not necessarily support multi-threading in the generation of the initial generation.
Abstract The benchmark for human-powered aircraft (hpa) design is introduced in the paper Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design. The original benchmark is available here. This package serves as a wrapper for the original benchmark.
APIs class ConstrainedProblem(problem_name: str, n_div: int = 4, level: int = 0 ) problem_name: The name of a benchmark problem. All problem names and their explanations are provided here. n_div: The wing segmentation number and alters the problem’s dimension.
Abstract The bbob-biobj test suite was created by combining existing 55 noiseless single-objective test functions. bbob-biobj (and its extension, bbob-biobj-ext) has in total of 92 (= original 55 + additional 37) bi-objective functions. Each benchmark function is provided in dimensions [2, 3, 5, 10, 20, 40] with 15 instances. In this package, all the 92 functions are available. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use.
Abstract The blackbox optimization benchmarking biobj-mixed-integer (bbob-biobj-mixint) test suite comprises 92 noiseless mixed-integer bi-objective test functions. Each benchmark function is provided in dimensions [5, 10, 20, 40, 80, 160] with 15 instances. Please refer to the paper for details about each benchmark function.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob benchmark function to use. It must be in the range of [1, 92].
Abstract This package provides a wrapper of the optproblems library’s DTLZ test suite, which consists of 7 kinds of continuous problems with variadic objectives and variables. For the details of the benchmark problems, please take a look at the original paper (Deb et al., 2001) in the reference section.
APIs class Problem(function_id: int, n_objectives: int, dimension: int, k: int, **kwargs: Any) function_id: Function ID of the WFG problem in [1, 9].
Abstract This package provides a wrapper of the optproblems library’s WFG test suite, which consists of 9 kinds of continuous problems with variadic objectives and variables. For the details of the benchmark problems, please take a look at the original paper (Huband et al., 2006) in the reference section.
APIs class Problem(function_id: int, n_objectives: int, dimension: int, k: int | None = None, **kwargs: Any) function_id: Function ID of the WFG problem in [1, 9].
Abstract This package provides a wrapper of the optproblems library’s ZDT test suite, which consists of 6 kinds of benchmark problems. For the details of the benchmark problems, please take a look at the original paper (Zitzler et al., 2000) in the reference section.
APIs class Problem(function_id: int, **kwargs: Any) function_id: Function ID of the WFG problem in [1, 9]. kwargs: Arbitrary keyword arguments, please refer to the optproblems documentation for more details.