This paper presents several strategies to tune the parameters of metaheuristic methods for (discrete) design optimization of reinforced concrete (RC) structures. A novel utility metric is proposed, based on the area under the average performance curve. The process of modelling, analysis and design of realistic RC structures leads to objective functions for which the evaluation is computationally very expensive. To avoid costly simulations, two types of surrogate models are used. The first one consists of the creation of a database containing all possible solutions. The second one uses benchmark functions to create a discrete sub-space of them, simulating the main features of realistic problems. Parameter tuning of four metaheuristics is performed based on two strategies. The main difference between them is the parameter control established to perform partial assessments. The simplest strategy is suitable to tune good `generalist' methods, i.e., methods with good performance regardless the parameter configuration. The other one is more expensive, but is well suited to assess any method. Tuning results prove that Biogeography-Based Optimization, a relatively new evolutionary algorithm, outperforms other methods such as GA or PSO for such optimization problems, due to its particular approach of applying recombination and mutation operators.
Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for non-convex global optimization
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology（哈尔滨工业大学机器人技术与系统国家重点实验室）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
University of California, Los Angeles, Los Angeles, CA, USA（加州大学洛杉矶分校）；Cognizant AI Lab, San Francisco, CA, USA（Cognizant AI实验室）；The University of Texas at Austin, Austin, TX, USA（德克萨斯大学奥斯汀分校）；Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院）
