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Metaheuristics are prominent gradient-free optimizers for solving hardproblems that do not meet the rigorous mathematical assumptions of analyticalsolvers. The canonical manual optimizer design could be laborious, untraceableand error-prone, let alone human experts are not always available. This arisesincreasing interest and demand in automating the optimizer design process. Inresponse, this paper proposes AutoOptLib, the first platform for accessibleautomated design of metaheuristic optimizers. AutoOptLib leverages computingresources to conceive, build up, and verify the design choices of theoptimizers. It requires much less labor resources and expertise than manualdesign, democratizing satisfactory metaheuristic optimizers to a much broaderrange of researchers and practitioners. Furthermore, by fully exploring thedesign choices with computing resources, AutoOptLib has the potential tosurpass human experience, subsequently gaining enhanced performance comparedwith human problem-solving. To realize the automated design, AutoOptLibprovides 1) a rich library of metaheuristic components for continuous,discrete, and permutation problems; 2) a flexible algorithm representation forevolving diverse algorithm structures; 3) different design objectives andtechniques for different optimization scenarios; and 4) a graphic userinterface for accessibility and practicability. AutoOptLib is fully written inMatlab/Octave; its source code and documentation are available athttps://github.com/qz89/AutoOpt and https://AutoOpt.readthedocs.io/,respectively.

AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design