受人类规划路线启发，提出新型优化算法PCIA，性能优于主流方法。
PCIA: A Path Construction Imitation Algorithm for Global Optimization
- 模仿人类构建路径的思维：融合现有路线生成新解
- 在53个数学问题和13个约束问题上表现优异，超越多数算法
- 适合解决复杂优化问题，尤其对路径搜索类任务有优势
本文提出一种新型元启发式优化算法——路径构建模仿算法（PCIA），其灵感来自人类构建并利用新路径的方式。通常，人类偏好已知的通行路线；当路径受阻时，会智能混合已有路径形成新路线；面对未知目的地时，则随机选择路径。PCIA通过生成随机种群来寻找最优路径，类似群体智能算法，每个粒子代表一条通向目标的路径。该算法在53个数学优化问题和13个约束优化问题上进行了测试，结果表明，PCIA在性能上与主流及最新元启发式算法相比具有高度竞争力。
原文摘要 · Abstract (English)
In this paper, a new metaheuristic optimization algorithm, called Path Construction Imitation Algorithm (PCIA), is proposed. PCIA is inspired by how humans construct new paths and use them. Typically, humans prefer popular transportation routes. In the event of a path closure, a new route is built by mixing the existing paths intelligently. Also, humans select different pathways on a random basis to reach unknown destinations. PCIA generates a random population to find the best route toward the destination, similar to swarm-based algorithms. Each particle represents a path toward the destination. PCIA has been tested with 53 mathematical optimization problems and 13 constrained optimization problems. The results showed that the PCIA is highly competitive compared to both popular and the latest metaheuristic algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。