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.

翻译：本文提出了一种新型的基于群体的元启发式算法——印度婚礼系统优化（IWSO），其灵感来源于传统印度婚礼中的社会文化动态。IWSO将家庭、候选人和媒人之间的协作驱动的匹配过程建模为一个有引导的选择性搜索框架，用于解决复杂优化问题。该算法引入了两大关键创新：（i）一种基于媒人引导的影响策略，其中精英解引导较弱候选者的演化，从而在无需外部参数的情况下增强收敛性；以及（ii）一种自适应淘汰与重初始化机制，通过替换表现不佳的个体来维持多样性并防止过早收敛。IWSO采用加权多目标适应度函数并分析了时间与空间复杂度，并将其与现有优化方法（如遗传算法（GA）、粒子群优化（PSO）、差分进化（DE）、布谷鸟搜索（CS）等）进行了基准测试。在标准高维和多模态测试函数上的大量实验表明，IWSO在收敛速度、解质量和鲁棒性方面均表现出卓越性能。