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Abstract:
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Recently discovered quantum cascade lasers (QCLs) have many potential uses, of particular note is their ability to be used as sensors at room temperature. These sensors can be tuned to detect chemical and biological agents, and have potential applications in many fields including medicine and security. But finding the right design solution can be challenging, especially for lasers that operate in the terahertz region. The design and production process is prohibitive, so an optimization algorithm is needed to find high quality designs. This research attempts to find high quality QCL designs using a parallel memetic multi-objective evolutionary algorithm with a local search. The memetic algorithm is able to find high quality solutions, but comparisons of results with and without local search show that the local search has a minimal effect on the solutions obtained. This is due in large part to the operators that are used in the baseline algorithm.
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