AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design

• URL: http://arxiv.org/abs/2303.06536v2
• Date: Tue, 14 Nov 2023 09:18:43 GMT
• Title: AutoOptLib: Tailoring Metaheuristic Optimizers via Automated Algorithm Design
• Authors: Qi Zhao, Bai Yan, Taiwei Hu, Xianglong Chen, Qiqi Duan, Jian Yang, Yuhui Shi
• Abstract summary: This paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristics. By fully exploring the design choices with computing resources, AutoOptLib has potential to surpass human experience.
• Score: 23.778407064391658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metaheuristics are prominent gradient-free optimizers for solving hard problems that do not meet the rigorous mathematical assumptions of analytical solvers. The canonical manual optimizer design could be laborious, untraceable and error-prone, let alone human experts are not always available. This arises increasing interest and demand in automating the optimizer design process. In response, this paper proposes AutoOptLib, the first platform for accessible automated design of metaheuristic optimizers. AutoOptLib leverages computing resources to conceive, build up, and verify the design choices of the optimizers. It requires much less labor resources and expertise than manual design, democratizing satisfactory metaheuristic optimizers to a much broader range of researchers and practitioners. Furthermore, by fully exploring the design choices with computing resources, AutoOptLib has the potential to surpass human experience, subsequently gaining enhanced performance compared with human problem-solving. To realize the automated design, AutoOptLib provides 1) a rich library of metaheuristic components for continuous, discrete, and permutation problems; 2) a flexible algorithm representation for evolving diverse algorithm structures; 3) different design objectives and techniques for different optimization scenarios; and 4) a graphic user interface for accessibility and practicability. AutoOptLib is fully written in Matlab/Octave; its source code and documentation are available at https://github.com/qz89/AutoOpt and https://AutoOpt.readthedocs.io/, respectively.

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