Emergency evacuation in densely populated facilities remains a pressing concern where human behavior under stress directly influences evacuation outcomes. A behaviorally-informed adaptive quantum ant colony optimization (BIAQACO) framework is proposed that integrates psychological stress coefficients and behavioral adaptation mechanisms into multi-objective evacuation planning. The model minimizes total evacuation duration while managing psychological congestion through anxiety-weighted path selection. An adaptive quantum rotation strategy adjusts exploration-exploitation balance based on real-time behavioral feedback, while mutation operators prevent premature convergence in complex psychological state spaces. Experiments across benchmark traveling salesman instances and campus evacuation scenarios validate framework effectiveness. Results demonstrate BIAQACO achieves 36.58% faster evacuation times and 43.23% lower psychological stress accumulation compared to the best-performing baseline algorithm under high-density conditions with one thousand evacuees.
Modern power systems face new challenges in maintaining stability and real-time balance with the large-scale integration of intermittent renewable energy. To address the persistent issue of insufficient participation in demand-side management, this study proposes a three-tier Stackelberg game coordination framework for Virtual Power Plants (VPPs) and Price-Based Demand Response (PBDR). To efficiently solve the high-dimensional nested multilevel optimization problem, a Hierarchical Nested Particle Swarm Optimization (HN-PSO) algorithm is developed. Theoretical analysis and simulation results demonstrate the existence and uniqueness of the global equilibrium in the proposed game model. A 24-hour simulation further shows that the VPP can mitigate price volatility while balancing internal dispatchable generation and external procurement. Therefore, the proposed approach can provide an efficient and practical computational method for decentralized market dispatch under renewable energy uncertainty.
The Honey Badger Algorithm (HBA) is a recent addition to the growing family of nature-inspired optimization techniques within the domain of Swarm Intelligence (SI). Inspired by the intelligent foraging behavior of honey badgers, HBA has demonstrated remarkable effectiveness in solving complex optimization problems. This review provides an analysis of HBA, exploring its core principles, applications (particularly within the engineering domain), and the evolution of the algorithm through various modified and hybrid versions. The authors further leverage bibliometric analysis to unveil research trends and offer valuable insights into the current state of HBA research. Finally, this study provides insights for future advancements and applications in diverse problem-solving tasks while harnessing the HBA.
The Squirrel Search Algorithm (SSA) has garnered considerable attention from the academic community and is increasingly applied across multiple disciplines to tackle a variety of optimization challenges since 2019. Inspired by the dynamic foraging behavior of southern flying squirrels, the SSA mimics their energy-efficient gliding locomotion—a special adaptive strategy that enables these small mammals to traverse large distances with minimal effort. This study offers a comprehensive analysis of the original SSA, along with its modifications, hybridizations, and variants. Furthermore, this study elucidated the practical applications of SSA across various engineering disciplines and incorporated analyses of studies demonstrating suboptimal SSA performance. Finally, the authors discuss future research directions to further enhance the SSA’s performance.
Predicting the short-term state of the Limit Order Book (LOB) is crucial in quantitative finance, yet challenging due to its high-dimensional and noisy nature. While deep learning models like Transformers excel at capturing temporal patterns, they often rely on suboptimal hyperparameters and lack robustness. This paper introduces PSO-Transformer, a hybrid framework for high-frequency LOB prediction. It combines a modified Particle Swarm Optimization (PSO) with adaptive inertia and a Temporal Attention Network. The framework operates through three dedicated modules: data preprocessing, PSO-based hyperparameter optimization, and temporal attention forecasting. Experiments on a large-scale LOB dataset show that PSO-Transformer outperforms state-of-the-art benchmarks, achieving an average Matthews Correlation Coefficient (MCC) of 0.612, an increase of 15.9% compared to the base line Transformer. Ablation and sensitivity studies validate each component's contribution and reveal emergent optimal model structures.
