Multi-objective gold rush optimization algorithm: Theoretical Extensions and applications in UAV path planning - Kecheng Su; Yaoyang Wang; Yikang Kong; Wenan Liu (PLoS ONE, 2026)
Multi-objective optimization problems have extensive application value in the fields of engineering and science, among which UAV path planning, as a typical application scenario, has attracted considerable attention. This study innovatively proposes a multi-objective extension of the Gold Rush Optimization algorithm (GRO), namely the Multi-Objective Gold Rush Optimization algorithm (MOGRO). By introducing a reference-point-guided two-level selection mechanism and an external archive strategy, the algorithm effectively addresses the challenge of obtaining Pareto-optimal solutions in multi-objective optimization problems. A systematic validation method is adopted: firstly, a comparative analysis is conducted between MOGRO and seven advanced multi-objective optimization algorithms on a benchmark test set consisting of 18 standard test problems. Subsequently, a UAV path planning multi-objective optimization model is constructed, taking into account both path length and obstacle threats, and the top four algorithms from the benchmark tests are selected for application validation. Experimental results show that the MOGRO algorithm significantly outperforms the comparison algorithms in terms of convergence, distribution, and solution quality, demonstrating excellent optimization performance. This study not only enriches the theoretical system of the GRO algorithm but also provides an innovative solution for UAV path planning in complex environments, with important theoretical value and practical significance.