The Fungal Growth Optimizer (FGO) is a nature-inspired metaheuristic that simulates fungal colony behaviors, but its exploitation phase can lose search diversity when guidance is dominated by limited peer or global-best information. In this paper, we propose an enhanced variant called the Comprehensive Learning Fungal Growth Optimizer (CLFGO). We integrate a conditionally activated Comprehensive Learning (CL) strategy into the FGO framework. When a candidate solution stagnates, the strategy constructs a dimension-specific learning exemplar. This mechanism allows each dimension to learn from the personal best of a different peer, extending the original fungal growth model. CLFGO is therefore intended for high-dimensional, multimodal, hybrid, and composition landscapes in which the original FGO is prone to diversity loss, rather than as a universal replacement for all problem classes. This approach improves population diversity and reduces the risk of premature convergence. We evaluated CLFGO on 29 CEC2017 benchmark functions at 30 dimensions against nine metaheuristics under the same maximum-function-evaluation budget. CLFGO achieved the lowest mean error on 21 of 29 functions and attained a Friedman average rank of 1.5517. Furthermore, we applied CLFGO to a reservoir production optimization problem, where it obtained a mean Net Present Value of $9.97\times {10}^8$ USD, outperforming the compared algorithms in both solution accuracy and convergence stability.

Ore districts are important targets for deep mineral exploration. As exploration depths increase, exploration strategies are shifting from ore-body identification based mainly on existing metallogenic models toward refined characterization of subsurface structures and deep mineralization targets. Owing to its high sensitivity to low-resistivity targets, the long-offset transient electromagnetic method (LOTEM) is an important geophysical tool for mineral exploration. However, complex geological settings and electromagnetic interference in ore districts often reduce interpretation accuracy and increase inversion non-uniqueness. To address these challenges, this study proposes an initial-model-guided 3D LOTEM inversion strategy. Background resistivity trends and structural information are incorporated into initial-model construction to provide a better-informed starting model before inversion. Taking concealed vein-type ore bodies in the deep part of the Zhaoxian ore district, northwestern Jiaodong, as the target, an approximate 3D geoelectrical model was built by integrating geological data, borehole information, profile interpretations, and electrical-property constraints. Based on this model, LOTEM forward-response characteristics, inversion comparisons, recovery behavior, and quantitative target-identification metrics were analyzed. The results show that the layered alteration background modifies the response of low-resistivity vein-type targets and reduces the distinguishability between ore-body anomalies and background structural responses, thereby affecting inversion recovery. The structure-constrained initial model concentrates model updates near the target zone and significantly improves anomaly recovery. Sensitivity tests further indicate that one-dimensional electrical information mainly constrains the approximate position and resistivity level of the low-resistivity anomaly, whereas layer-interface information improves the spatial correspondence between the recovered anomaly, the layered background, and the ore-body extension direction. When both types of prior information are integrated, ore-body recovery is significantly enhanced. Under the synthetic-model conditions considered in this study, the initial-model optimization strategy improves the recovery of concealed vein-type low-resistivity targets in a layered alteration background and may inform subsequent LOTEM survey design and inversion interpretation in the Zhaoxian ore district.