The N-Queens challenge is a prominent combinatorial optimization test requiring queens to be placed on an N × N chessboard without attacking positions. Traditional threat-minimization approaches often yield suboptimal results due to limited options requiring large movements. The adopted Particle Swarm Optimization (PSO) achieves multi-objective optimization with adaptability. The proposed solution reduces risks, minimizes movements, and expands solution diversity. Fast convergence is ensured through inertia weight control, velocity constraints, and a mutation method to enhance exploration and efficiency. Experimental results show that adaptive PSO reduces threats more effectively than random methods while maintaining higher diversity, improving threat mitigation by 60%. The approach is effective for dynamic multi-objective combinatorial optimization, successfully applying to complex scenarios beyond initial testing boundaries.