A Human Behavior Optimization Algorithm Based on Legal and Ethical Constraints for Numerical Optimization and Practical Applications

This paper proposes an improved metaheuristic algorithm named LHBBO, which incorporates legal and moral constraints into a human behavior-based optimization framework to tackle the limitations of conventional methods in high-dimensional and multimodal problem spaces. Three key innovations are introduced: a dual-layer normative audit mechanism that enforces hard legal and soft moral constraints during candidate evaluation; a jury-guided collaborative consultation strategy that diversifies search direction references; and a directional migration mechanism triggered by population diversity and stagnation metrics. The proposed LHBBO is evaluated on the CEC2017 and CEC2022 benchmark suites, where it demonstrates significantly better convergence behavior and solution quality compared to several state-of-the-art algorithms. Notably, in 100-dimensional tests, LHBBO improves optimization precision by over 97% relative to the standard HBBO. When applied to unsupervised visual anomaly detection in industrial settings using the M2AD dataset, LHBBO effectively optimizes key parameters of a collaborative discrepancy-based model. The resulting system achieves a 9.05% increase in pixel-level localization accuracy (AUPRO) compared to the baseline CDO model, confirming its practical utility in complex, non-convex industrial optimization tasks.