PSOT: Elite-Driven optimization via the Pareto principle for scalable engineering design solutions

Abstract

Optimizing complex real-world problems demands efficient, scalable solutions across diverse domains. This paper presents the Pareto Search Optimization Technique (PSOT), a novel population-based metaheuristic algorithm designed to address intricate optimization challenges by integrating the Pareto principle (80/20 Rule) into its core framework. PSOT innovatively partitions the population into elite (top 20%) and non-elite groups through systematic evaluation and dynamic sorting, strategically directing computational resources toward high-potential solutions in the elite cohort. This elite-centric search mechanism aligns with the Pareto principle, prioritizing exploration of the most promising regions of the solution space to enhance convergence rates while maintaining diversity. The algorithm’s uniqueness lies in its adaptive balance of intensification and diversification, leveraging the elite group to accelerate discovery of near-optimal solutions without premature stagnation. To validate PSOT’s efficacy, comprehensive experiments were conducted on standardized benchmark functions and real-world engineering design problems, including constrained, multi-modal, and high-dimensional scenarios. Results demonstrate PSOT’s superior performance against state-of-the-art optimization methods, achieving statistically significant improvements in solution quality (12–28% gains in objective values) and computational efficiency (35–50% reduction in function evaluations). Case studies across mechanical, structural, and energy systems further underscore PSOT’s practical applicability, consistently delivering robust, high-quality solutions. The algorithm’s scalability, simplicity, and alignment with empirical efficiency principles position it as a versatile tool for researchers and practitioners tackling complex optimization landscapes.