受印度婚礼文化启发，提出新型优化算法IWSO，提升求解效率与稳定性。
Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis
- 模仿家庭、候选人与婚介协作匹配，构建引导式搜索框架。
- 收敛速度快，高维多峰测试中解的质量优于传统算法。
- 适合复杂优化问题，尤其对收敛性与多样性要求高的场景。
本文提出一种受传统印度婚礼社会文化启发的新颖群体智能优化算法——印度婚礼系统优化（IWSO）。该算法将家庭、候选人及婚介间的协作匹配过程建模为引导式、选择性搜索框架，用于解决复杂优化问题。核心创新包括：(i) 婚介引导的影响策略，由优质解指导劣解进化，加速收敛且无需外部参数；(ii) 自适应淘汰与重初始化机制，通过替换表现不佳个体维持种群多样性，防止早熟收敛。IWSO采用加权多目标适应度函数，并给出解析的时空复杂度分析。在标准高维、多峰测试函数上，与遗传算法（GA）、粒子群优化（PSO）、差分进化（DE）、布谷鸟搜索（CS）等方法对比，实验表明其在收敛速度、解质量与鲁棒性方面均表现更优。
原文摘要 · Abstract (English)
This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of traditional Indian weddings. IWSO models the matchmaking process driven by collaboration among families, candidates, and matchmakers as a guided, selective search framework for solving complex optimization problems. The algorithm introduces two key innovations: (i) a matchmaker-guided influence strategy, where elite solutions direct the evolution of weaker candidates, enhancing convergence without external parameters; and (ii) an adaptive elimination and reinitialization mechanism that maintains diversity and prevents premature convergence by replacing underperforming individuals. IWSO employs a weighted multi-objective fitness function and analytically derived time and space complexity, benchmarked against existing optimization approaches such as Genetic Algorithm (GA), Partical Swarm Optimization (PSO), Differential Evolution (DE), Cuckoo Search (CS), etc. Extensive experiments on benchmark high-dimensional and multimodal test functions demonstrate superior performance of IWSO in terms of convergence speed, solution quality, and robustness.
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