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ExpensiveOptimBenchmark reviews and mentions
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29 Python real world optimization tutorials
For the problems with continous decision variables it is not trivial to come up with faster approaches on a modern many-core CPU. But even with discrete input (scheduling and planning) new continous optimizers can compete. The trick is to utilize parallel optimization runs and numba to perform around 1E6 fitness evaluations each second. Advantage is that it is much easier to create a fitness function than for instance to implement incremental score calculation in Optaplanner. And it is more flexible if you have to handle non-standard problems. For very expensive optimizations (like https://github.com/AlgTUDelft/ExpensiveOptimBenchmark) parallelization of fitness evaluation is more important than to use surrogate models.
Stats
AlgTUDelft/ExpensiveOptimBenchmark is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of ExpensiveOptimBenchmark is Python.
