Feasibility-guaranteed multi-objective evolutionary path planning with knowledge-based local guidance

Abstract

Multi-objective path planning is an important yet challenging problem for mobile agents working in complex environments with obstacles and risks. While evolutionary algorithms show significant promise, they often struggle to guarantee collision-free paths. To address this, we propose a novel multi-objective evolutionary path planning framework that ensures path feasibility and employs two local guidance operators to accelerate convergence. First, a two-stage constraint-handling mechanism is designed to ensure path feasibility. It resolves point violations in which waypoints lie within obstacles via a remapping strategy, and corrects segments that cross obstacles via a deterministic correction strategy. This ensures all individuals in the population represent feasible, i.e., obstacle-free, paths, enabling accurate fitness evaluation without penalty functions. Second, we design two local guidance operators that leverage problem-specific knowledge to guide elite solutions toward desirable regions in the objective space, thereby accelerating convergence to the extremities of the Pareto front. These operators guide the elite solutions, stored in two separate archives for path length and risk respectively, toward shorter or safer regions before reintegrating them into the population with an adapted replacement strategy. Extensive experiments on 30 randomly generated problems demonstrate the superiority of the proposed algorithm. Furthermore, ablation studies confirm that both the constraint-handling mechanism and the local guidance operators perform as expected.