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首页> 外文会议>International Workshop on Engineering Stochastic Local Search Algorithms(SLS 2007); 20070906-08; Brussels(BE) >Implementation Effort and Performance: A Comparison of Custom and Out-of-the-Box Metaheuristics on the Vehicle Routing Problem with Stochastic Demand
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Implementation Effort and Performance: A Comparison of Custom and Out-of-the-Box Metaheuristics on the Vehicle Routing Problem with Stochastic Demand

机译：实施力度和性能：带有随机需求的车辆路径问题的自定义和开箱即用的元启发法比较

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摘要

In practical applications, one can take advantage of metaheuristics in different ways: To simplify, we can say that metaheuristics can be either used out-of-the-box or a custom version can be developed. The former way requires a rather low effort, and in general allows to obtain fairly good results. The latter implies a larger investment in the design, implementation, and fine-tuning, and can often produce state-of-the-art results. Unfortunately, most of the research works proposing an empirical analysis of metaheuristics do not even try to quantify the development effort devoted to the algorithms under consideration. In other words, they do not make clear whether they considered out-of-the-box or custom implementations of the metaheuristics under analysis. The lack of this information seriously undermines the generality and utility of these works. The aim of the paper is to stress that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. As a case study, we focus on the vehicle routing problem with stochastic demand and on five among the most successful metaheuristics-namely, tabu search, simulated annealing, genetic algorithm, iterated local search, and ant colony optimization. We show that the relative performance of these algorithms strongly varies whether one considers out-of-the-box implementations or custom ones, in which the parameters are accurately fine-tuned.
机译：在实际应用中，可以通过多种方式利用元启发法：为简化起见，我们可以说元启发法可以直接使用，也可以开发自定义版本。前一种方法需要相当少的努力，并且通常可以获得相当好的结果。后者意味着在设计，实施和微调上的大量投资，并且通常可以产生最新的结果。不幸的是，大多数提出对元启发式方法进行实证分析的研究工作甚至都没有试图量化致力于该算法的开发工作。换句话说，他们不清楚他们是否考虑了现成的或正在分析的元启发式的自定义实现。缺少这些信息严重破坏了这些作品的通用性和实用性。本文的目的是强调，开箱即用的实现所获得的结果不能总是推广到自定义的实现，反之亦然。作为案例研究，我们关注具有随机需求的车辆路径问题，并研究最成功的五种启发式方法中的五个，即禁忌搜索，模拟退火，遗传算法，迭代局部搜索和蚁群优化。我们表明，无论是考虑现成的实现方式还是定制的实现方式，在这些实现方式中对参数进行了精确的微调，这些算法的相对性能都存在很大差异。

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