Comparative Analysis of Population Initialization Strategies in Metaheuristic Optimization

Abstract Metaheuristic algorithms depend on population initialization to ensure an effective balance between exploration and exploitation in complex search spaces. Randomly initialized populations result in uneven coverage and premature convergence, especially in high-dimensional or multimodal problem landscapes. In this study, we investigate traditional and seven advanced population initialization techniques, including quasi-random sequence, chaotic map, opposition-based, knowledge-based, hybrid, and machine learning-based approaches. We discuss their theoretical foundations, computational complexity, and practical effectiveness. Findings suggest quasi-random initialization improves convergence for medium-dimensioned problems, chaotic population initialization improves convergence for multimodal search landscapes, and machine learning techniques adapt to very-high-dimensional optimization problems. Advanced techniques increase computational cost but significantly improve convergence rate and algorithm robustness. Such insights can assist researchers and practitioners in selecting appropriate initialization techniques for the complexity of their problems, and also indicate the direction of future research into intelligent and adaptive initialization techniques.