改进猫群算法，提升多目标优化的收敛与多样性
Multi-objective Cat Swarm Optimization Algorithm based on a Grid System
- 用贪心策略替代轮盘赌，优化搜索方向选择
- 引入网格系统与双存档机制，保持解集分布均匀
- 在压力容器设计等场景中表现优于主流算法
本文提出一种基于网格系统的多目标猫群优化算法（GMOCSO），以实现收敛性与多样性并重。针对原猫群算法采用轮盘赌选择导致搜索效率低的问题，改用贪心策略；借鉴帕累托存档进化策略（PAES）中的网格系统和双存档机制，有效维持解集分布广度与多样性。通过多个测试函数及真实世界问题——压力容器设计问题，验证算法性能。与多种经典算法比较，使用逆生成距离（Reversed Generational Distance）、间距指标（Spacing metric）和分布度量（Spread metric）评估，结果表明本算法具有更强鲁棒性，统计分析与图表进一步支持结论。
原文摘要 · Abstract (English)
This paper presents a multi-objective version of the Cat Swarm Optimization Algorithm called the Grid-based Multi-objective Cat Swarm Optimization Algorithm (GMOCSO). Convergence and diversity preservation are the two main goals pursued by modern multi-objective algorithms to yield robust results. To achieve these goals, we first replace the roulette wheel method of the original CSO algorithm with a greedy method. Then, two key concepts from Pareto Archived Evolution Strategy Algorithm (PAES) are adopted: the grid system and double archive strategy. Several test functions and a real-world scenario called the Pressure vessel design problem are used to evaluate the proposed algorithm's performance. In the experiment, the proposed algorithm is compared with other well-known algorithms using different metrics such as Reversed Generational Distance, Spacing metric, and Spread metric. The optimization results show the robustness of the proposed algorithm, and the results are further confirmed using statistical methods and graphs. Finally, conclusions and future directions were presented..
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