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Photonic inverse design has emerged as an indispensable engineering tool forcomplex optical systems. In many instances it is important to optimize for bothmaterial and geometry configurations, which results in complex non-smoothsearch spaces with multiple local minima. Finding solutions approaching globaloptimum may present a computationally intractable task. Here, we develop aframework that allows expediting the search of solutions close to globaloptimum on complex optimization spaces. We study the way representative blackbox optimization algorithms work, including genetic algorithm (GA), particleswarm optimization (PSO), simulated annealing (SA), and mesh adaptive directsearch (NOMAD). We then propose and utilize a two-step approach that identifiesbest performance algorithms on arbitrarily complex search spaces. We reveal aconnection between the search space complexity and algorithm performance andfind that PSO and NOMAD consistently deliver better performance for mixedinteger problems encountered in photonic inverse design, particularly with theaccount of material combinations. Our results differ from a commonlyanticipated advantage of GA. Our findings will foster more efficient design ofphotonic systems with optimal performance.

A Framework for Discovering Optimal Solutions in Photonic Inverse Design