Metaheuristics are gradient-free and problem-independent search methods. They
have gained huge success in solving various optimization problems in academia
and industry. Automated metaheuristic algorithm design is a promising
alternative to human-made design. This paper proposes a methodological
framework, AutoOpt, for automatically designing metaheuristic algorithms for
optimization problems. AutoOpt consists of: (1) a bi-level criterion to
evaluate the designed algorithms' performance; (2) a general schema of the
decision space from where the algorithms will be designed; (3) a mixed graph-
and real number-based representation to represent the designed algorithms; and
(4) a model-free method to conduct the design process. AutoOpt benefits
academic researchers and practical users struggling to design metaheuristic
algorithms for optimization problems. A real-world case study demonstrates
AutoOpt's effectiveness and efficiency.

本研究针对优化问题，提出了一种基于自动设计元启发式算法的方法学框架 AutoOpt，该框架包含具有良好性能的算法性能评估标准、算法设计决策空间的一般架构、设计算法所用的混合图和实数表示以及无模型方法来进行设计过程。实测结果表明，AutoOpt 是一种有效和高效的设计元启发式算法的方法学框架。