比较仿生优化算法的方法论指南处方
A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms
AI总结：
针对仿生优化新算法比较验证不规范、结果显著性不足的问题，本文综述文献建议并提出方法论指南，以帮助作者、审稿人和编辑规范算法提案与评估。
摘要：
仿生优化（包括进化计算和群体智能）是一个不断发展的研究课题，每年都有许多具有竞争力的仿生算法被提出。在这样一个活跃的领域中，准备一份成功的新型仿生算法提案并非易事。鉴于该研究领域的成熟度，提出一种具有创新元素的新型优化技术已不再足够。除了新颖性之外，作者报告的结果还应被证明相较于现有技术的先前成果取得了显著进展。遗憾的是，并非所有新提案都能妥善处理这一要求。其中一些未能选择合适的基准测试或参考算法进行比较。在其他情况下，所执行的验证过程没有以有原则的方式定义（甚至根本没有进行验证）。因此，此类研究中所呈现结果的显著性无法得到保证。在本工作中，我们回顾了文献中的若干建议，并考虑到所有这些问题，提出了用于准备一份成功提案的方法论指南。我们期望这些指南不仅对作者有用，也能为审稿人和编辑评估该领域的新贡献提供帮助。
英文摘要：
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.
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。
