This paper introduces multi-directional local search, a metaheuristic for multi-objective optimization. We first motivate the method and present an algorithmic framework for it. We then apply it to several known multi-objective problems such as the multi-objective multi-dimensional knapsack problem, the bi-objective set packing problem and the bi-objective orienteering problem. Experimental results show that our method systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort. We conclude that the proposed algorithmic framework is a viable option when solving multi-objective optimization problems.

中文翻译：

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多向本地搜索

本文介绍了多向局部搜索，这是一种用于多目标优化的元启发式方法。我们首先激发该方法并为其提供算法框架。然后，我们将其应用于几个已知的多目标问题，例如多目标多维背包问题、双目标集包装问题和双目标定向运动问题。实验结果表明，我们的方法在合理的 CPU 工作量范围内，系统地提供了与应用于这些问题的基准实例的最先进方法相媲美的解决方案集。我们得出结论，在解决多目标优化问题时，所提出的算法框架是一个可行的选择。