Impact of Population Initialization Strategies on PSO Performance for Job Shop Scheduling Problems

Abstract

Population initialization significantly influences metaheuristic algorithm performance, yet random initialization dominates despite problem-specific needs. This study investigates initialization strategies for the Job Shop Scheduling Problem (JSSP), an NP-hard combinatorial optimization challenge in manufacturing systems, addressing the gap in understanding how different initialization approaches affect solution quality and reliability in constrained discrete problems. The research employs a two-phase experimental design using Particle Swarm Optimization (PSO) on Taillard benchmark instances. Phase 1 evaluates seventeen initialization methods across four categories: random-based, problem-specific heuristics, hybrid methods, and adaptive strategies. Each method is tested through 30 independent runs on six problem instances. Phase 2 develops machine learning-enhanced initialization using variational autoencoders (VAEs) trained on high-quality solutions from successful traditional methods, comparing three VAE variants against conventional approaches. Results show all random-based methods failed completely, while only First-In-First-Out and Most Work Remaining heuristics succeeded consistently among traditional approaches. VAE-based methods achieved 100% solution validity (540/540) versus 97% for traditional methods (349/360), with statistical significance (χ2 = 14.27, p < 0.001). The Friedman test confirmed performance differences (χ2 = 19.87, p < 0.001, Kendall’s W = 0.828), with VAE methods achieving lower mean ranks and makespan reductions. Despite starting with lower initial diversity, VAE methods exhibited larger diversity increases during optimization, suggesting structured initialization enables more effective exploration than random dispersion.