RL-SOMA: Reinforcement learning control of the Self-Organizing Migrating Algorithm for global optimization and UAV path planning

The Self-Organizing Migrating Algorithm (SOMA) moves candidate solutions along discretized trajectories towards leader individuals, under two sensitive control parameters and a migration strategy fixed before the run. No single setting suits every landscape or every phase of a run. We propose RL-SOMA, in which a tabular Q-learning controller picks, at every generation, an operator and its parameter preset from a memetic portfolio of six operators, conditioned on a discretized state of population diversity, stagnation and budget progress, and warm-started by a progress-aware informed prior so that learning refines a competent schedule within one run. On the classical 13-function suite at D = 30 RL-SOMA leads the SOMA family, winning at least 10 of 13 functions against every SOMA variant, and ranks third of the eight methods behind GWO and L-SHADE, with all three inside the critical difference. Translating the optima of the nine centre-optimum functions under five independent offsets shows GWO’s lead to be geometric: its median result degrades by 10.6 orders of magnitude, while RL-SOMA is unchanged. On CEC2017 at D = 30 RL-SOMA ranks 3.72, indistinguishable from SOMA-T3A and DE, with L-SHADE ahead of the field. On the classical suite at D = 50 it ranks second (2.62), behind GWO (1.96) and ahead of L-SHADE (3.04). On constrained three-dimensional UAV path planning over five spline-parameterized scenarios RL-SOMA attains the best average rank among seven planners, L-SHADE included (1.60), is never significantly outperformed on any scenario, and holds 100% feasibility on every map under both the collision-free and the stricter margin criterion, where PSO falls to 55% and GWO to 80%. An ablation with a frozen-prior baseline separates the two learning components: the prior carries most of the gain, while online learning adds a significant further improvement on three of six probes and degrades none.