Hybridization plays a prominent role in bolstering the performance of optimization algorithms (OAs), yet designing efficient hybrid OAs tailored to intricate optimization problems persists as a formidable task. This article introduces a novel top-down methodology for the automated design of hybrid OAs, treating algorithm design as a meta-optimization problem. A general design template for collaboration-based hybrid OAs is developed, integrating a multitude of hybridization strategies for the first time. Besides, a mathematical model is built to formulate the meta-optimization problem of algorithm design. To address the meta-optimization challenge, an improved multifactorial evolutionary algorithm is proposed to automatically design efficient hybrid metaheuristics in a multitasking environment for the given instances with diverse features. To verify the effectiveness of the proposed design methodology, it is applied to the CEC2017 benchmark functions and the binary knapsack problem. Numerical results have demonstrated the feasibility and effectiveness of the proposed methodology for both continuous and combinatorial optimization benchmarks.

中文翻译：

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基于协作的混合元启发法的自动化设计


混合在增强优化算法 (OA) 性能方面发挥着重要作用，但设计针对复杂优化问题的高效混合 OA 仍然是一项艰巨的任务。本文介绍了一种用于混合 OA 自动化设计的新颖的自上而下方法，将算法设计视为元优化问题。开发了基于协作的混合办公自动化的通用设计模板，首次集成了多种混合策略。此外，还建立了数学模型来制定算法设计的元优化问题。为了解决元优化挑战，提出了一种改进的多因素进化算法，可以在多任务环境中为具有不同特征的给定实例自动设计高效的混合元启发式算法。为了验证所提出的设计方法的有效性，将其应用于CEC2017基准函数和二进制背包问题。数值结果证明了所提出的方法对于连续和组合优化基准的可行性和有效性。