Efficient Multi-Objective Planning with Weighted Maximization Using Large Neighbourhood Search

• URL: http://arxiv.org/abs/2604.04826v1
• Date: Mon, 06 Apr 2026 16:27:18 GMT
• Title: Efficient Multi-Objective Planning with Weighted Maximization Using Large Neighbourhood Search
• Abstract summary: Autonomous navigation often requires simultaneous simultaneous optimization of multiple objectives.<n>An alternative weighted sum navigation method can find all the maximum solutions.<n>We propose a novel option based on the Large Search that efficiently achieves the weighted planning problem.
• Score: 10.971760362454793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous navigation often requires the simultaneous optimization of multiple objectives. The most common approach scalarizes these into a single cost function using a weighted sum, but this method is unable to find all possible trade-offs and can therefore miss critical solutions. An alternative, the weighted maximum of objectives, can find all Pareto-optimal solutions, including those in non-convex regions of the trade-off space that weighted sum methods cannot find. However, the increased computational complexity of finding weighted maximum solutions in the discrete domain has limited its practical use. To address this challenge, we propose a novel search algorithm based on the Large Neighbourhood Search framework that efficiently solves the weighted maximum planning problem. Through extensive simulations, we demonstrate that our algorithm achieves comparable solution quality to existing weighted maximum planners with a runtime improvement of 1-2 orders of magnitude, making it a viable option for autonomous navigation.

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