Benchmark Papers

A Benchmark Study of Optimization Search Algorithms
A thorough study was conducted that benchmarks the efficiency and robustness of several optimization algorithms. In particular, the hybrid adaptive method, SHERPA, in HEEDS MDO, was compared to several existing methods. These algorithms were tested on a broad set of benchmark problems, each of which emphasizes a different set of features commonly found in engineering optimization problems. It was concluded that the SHERPA algorithm is significantly more efficient and robust for these problems than the other methods in the study.
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A Benchmark Study of Multi-Objective Optimization Methods
A thorough study was conducted to benchmark the performance of several algorithms for multi-objective Pareto optimization. In particular, the hybrid adaptive method MO-SHERPA, in HEEDS Professional, was compared to the NCGA and NSGA-II methods.
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SHERPA - An Efficient and Robust Optimization/Search Algorithm

One of the keys to a successful optimization study is the effectiveness of the search algorithm used. This paper provides brief answers to the following questions about optimization algorithms: What does it mean for an algorithm to be efficient and robust, and why is it important? How do various algorithms compare on these important characteristics? What makes the algorithm SHERPA so effective?
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