Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis

TL;DR

This paper introduces IWSO, a new metaheuristic inspired by Indian weddings, which improves optimization performance through innovative influence and diversity mechanisms, outperforming existing algorithms.

Contribution

The paper proposes a novel socio-culturally inspired metaheuristic with unique influence and diversity strategies, validated by extensive benchmarking against established algorithms.

Findings

IWSO achieves faster convergence than GA, PSO, DE, and CS.

IWSO finds higher quality solutions on benchmark functions.

IWSO maintains diversity and avoids premature convergence effectively.

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.