Abstract: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.
| Subjects: | Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13871 [cs.NE] |
| (or arXiv:2605.13871v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13871 arXiv-issued DOI via DataCite |
Submission history
From: Jatinder Kumar [view email]
[v1]
Tue, 5 May 2026 08:57:52 UTC (681 KB)