CDDO-HS：儿童绘画发展优化和声搜索算法
CDDO-HS:Child Drawing Development Optimization Harmony Search Algorithm
AI总结：
针对CDDO算法探索性能差和局部最优解停滞的问题，提出混合CDDO与HS的CDDOHS算法，通过引入HS增强探索阶段，实验证明其优于CDDO、HS等八种算法。
摘要：
儿童绘画发展优化（CDDO）是元启发式算法的一个近期实例。发明该方法的动机源于儿童的学习行为和认知发展，并采用黄金比例来优化其艺术作品的美学价值。遗憾的是，CDDO在探索阶段表现不佳，且局部最优解停滞不前。和声搜索（HS）相对于其他流行的元启发式算法是一种极具竞争力的算法，因为其在单峰基准函数上的探索阶段表现非常出色。因此，为了避免这些问题，我们提出了CDDOHS，它是CDDO和HS两种标准的混合。所提出的混合模型包含两个阶段。首先，将模式大小（PS）移至算法的核心，并将初始模式大小设置为种群总大小的80%。其次，将标准HS添加到PS中用于探索阶段，以在每次迭代后增强和更新解。实验使用两个不同的标准基准函数（即经典测试函数）进行评估，包括23个常见函数和10个CEC-C06 2019函数。此外，将建议的CDDOHS与CDDO、HS及其他六种广泛使用的算法进行了比较。使用Wilcoxon秩和检验，结果表明CDDOHS优于其他算法。
英文摘要：
Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children's learning behavior and cognitive development, with the golden ratio employed to optimize their artwork's aesthetic value. Unfortunately, CDDO suffers from low performance in the exploration phase, and the local best solution stagnates. Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding. Thus, to avoid these issues, we present CDDOHS, a hybridization of both standards of CDDO and HS. The hybridized model proposed consists of two phases. Initially, the pattern size (PS) is relocated to the algorithm's core and the initial pattern size is set to 80 % of the total population size. Second, the standard harmony search (HS) is added to the pattern size (PS) for the exploration phase to enhance and update the solution after each iteration. Experiments are evaluated using two distinct standard benchmark functions, known as classical test functions, including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the suggested CDDOHS is compared to CDDO, HS, and six other widely used algorithms. Using the Wilcoxon ranksum test, the results indicate that CDDOHS beats alternative algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
