Raid Team Strategy (RTS): A Leader–Follower Metaheuristic with Team Merging for Multi-Basin Global Optimization

This paper proposes the Raid Team Strategy (RTS), a population-based metaheuristic inspired by the tactical organization of raid teams in search-and-attack operations. At each iteration, the best-ranked agents act as leaders and exploit their neighborhood through self-adaptive local search. In contrast, the remaining agents act as followers that are pulled toward the nearest leader along its direction of travel. The number of leaders is a fixed fraction of the population, while the number of teams that search separate regions changes during the run. Leaders that converge onto the same region are merged, and the weaker one is re-deployed at a random location; isolated but competitive followers are temporarily promoted to scouts, and stagnant followers are released to unexplored regions. RTS was evaluated under an equal budget of objective-function evaluations against ten methods, including the state-of-the-art L-SHADE, jSO and IPOP-CMA-ES and the deterministic DIRECT and DIRECT-L, on the CEC2017 (D = 10, 30, 50) and CEC2022 (D = 10, 20) suites with 51 independent runs, on the eight standard GKLS classes (800 randomly generated multi-extremal functions), and on six engineering design problems. RTS was significantly better than PSO, GWO, BA, and HHO on the CEC suites and statistically tied with SBOA, but it was clearly outperformed by L-SHADE, jSO, and IPOP-CMA-ES on the CEC suites and on the engineering problems. On the GKLS classes, RTS solved 638 of the 800 functions, compared with 260, 261, and 108 for L-SHADE, jSO, and IPOP-CMA-ES, and was exceeded only by the two DIRECT methods and, slightly, by HHO. An ablation study showed that the follower refinement and the focused probes are the components with a significant effect, and a sensitivity analysis showed that the results are robust to the parameter settings. The results position RTS as a method for landscapes with several separated basins rather than a fine local exploiter.