AI-Facilitated Ambient Factor-Based Annealment and Resiliency Sufficiency in Austere Locales

Ascertainment regarding Ambient Factor-Based (AFB) criteria weights is an intricate issue for Decision Engineering problems involving power grid substations. Resiliency Sufficiency Assessments often utilize Multi-Index Assessment Framework approaches that have counterpoised goals, and this is further complicated by AFB Condition Monitoring Paradigm considerations. This paper posits that a bespoke Adaptive Assessment & Weighting Methodology (A2WM) to derive entropy weights, operationalized by a Constriction Factor Particle Swarm Optimization-based Convolutional Neural Network using a bespoke numerical implementation and series of transformations, can help mitigate against the selection bias introduced when the A2WM utilizes the outputs of various Subjective Methods prior to hybridizing them with various Objective Methods. Preliminary results indicate that the referenced amalgam potentially has higher reliability/stability, consistency, as well as performance, thereby warranting further investigation.