Landscape-Aware Explainable Multi-Population Particle Swarm Optimization for Time Series Forecasting

Nonstationary time-series forecasting in critical applications needs an efficient accuracy level in the presence of concept drift; however, metaheuristics such as Multi-Population Quantum-inspired Particle Swarm Optimization (multiPQPSO) algorithm work in the form of a black box algorithm using static parameter settings. This work proposes a Landscape-Aware Explainable Multi-Population PSO (LAE-multiPQPSO) framework to tackle inefficient adaptation and lack of interpretability resulting from such non-transparent algorithms with static parameter settings. The proposed approach entails an online landscape analysis module, which calculates lightweight metrics of spatial ruggedness, normalized dimensional diversity, and convergence speed using swarm particle positions. These metrics adaptively tune hyperparameter limits (like fitness degradation limit, quantum contraction factor, and subswarm assignments) and generate rule-based explanations triggered via a JSON record structure. The proposed framework was evaluated using electric load, compute workload, epidemiological, and financial data sets, LAE-multiPQPSO outperforms benchmark models. The findings show that online landscape awareness gives dynamic parameter stability, fast concept drift adaptation, and clear explainability. Keywords— Concept Drift: Explainable Metaheuristics: Fitness Landscape Analysis: Particle Swarm Optimization: Time Series Forecasting