Pareto-Guided Leader Selection in the Mountain Gazelle Optimizer: A Controlled Multi-Objective Study

Where should multi-objective reasoning enter a swarm optimizer: before the search through scalarization, around the search through survival or archiving, or inside the search through Pareto-informed leader selection? We study this question on the Mountain Gazelle Optimizer (MGO) and propose a feedback-path taxonomy that separates the three roles. A census of twenty multi-objective MGO extensions finds one prior leader-mediated design and no method that assigns per-agent leaders from a Pareto archive. We develop four leader-mediated variants on one search skeleton, with a random-leader control and a leader-isolation ablation. On twenty-two benchmark problems (thirty runs each), all four variants rank ahead of five paper-based reconstructions of prior MGO designs on three indicators, and the decomposition and indicator leader rules outrank random leadership on hypervolume and IGD${}^{+}$ under Holm correction. On fourteen held-out engineering problems, a refined indicator-guided design separates from NSGA-II, NSGA-III, MOEA/D, AGE-MOEA-II and SMS-EMOA on IGD${}^{+}$ under returned-set scoring, and from all but SMS-EMOA on hypervolume; under evaluation-trace scoring, only from NSGA-III and MOEA/D on IGD${}^{+}$. In sample, of the five it separates only from MOEA/D ($\epsilon$-indicator, returned-set scoring). The four variants outperform the prior MGO designs as complete implementations; against established algorithms, the advantage depends on scoring.