In this paper, we study how metaheuristic optimizers tune Membership Functions (MFs) in Fuzzy Logic Systems (FLSs). We propose FuzzyMapX, an explainable framework for operator contribution analysis that extends EvoMapX. FuzzyMapX provides three complementary outputs: Operator Attribution Matrix (OAM), Population Evolution Graph (PEG), and lineage-aware Convergence Driver Score (CDS) that captures both immediate and delayed operator influence. We evaluate Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Cuckoo Search (CS) on three regression benchmarks (Combined Cycle Power Plant (CCPP), Friedman #2, and Mackey–Glass). Across 30 independent runs per dataset, DE and PSO achieve the best average RMSE, with statistically significant differences relative to GA and CS (Friedman test; Wilcoxon signed-rank test with Holm correction). The explanatory outputs show different operator contributions such as diminishing gains in GA toward late stages, a shift toward socially guided updates in PSO, crossover-dominated progress in DE, and restart-associated bursty gains in CS. FuzzyMapX provides interpretable process for MF optimization and can support explainable FLS design.