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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Multi-objective four dimensional imprecise TSP solved with a hybrid multi-objective ant colony optimization-genetic algorithm with diversity
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Multi-objective four dimensional imprecise TSP solved with a hybrid multi-objective ant colony optimization-genetic algorithm with diversity

机译：具有多样性的混合多目标蚁群优化 - 遗传算法解决多目标四维不精确TSP

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摘要

In real world, most of the combinatorial optimization problems are multi-objective and it is difficult to optimize them simultaneously. In the literature, some individual algorithms (ACO, GA, etc.) are available to solve such discrete multi-objective optimization problems (MOOPs), particularly travelling salesman problems (TSPs) . Here a hybrid algorithm combining ACO and GA with diversity is developed to solve discrete multi-objective TSPs and named MOACOGAD. Generally in TSP, routes for travel are not considered as lengths of routes remain unaltered. In real life, there may be several routes for travel from one destination to another and conditions of those routes may also be different such as good, rough, bad, etc. In practical, travel costs and travel times are not defined precisely and represented by fuzzy data. When fuzzy travel costs and fuzzy travel times per unit length are involved, the lengths and conditions of the routes along-with the types of conveyances for travel become important. In some cases, risk of travel is also involved. In this paper a four dimensional imprecise TSP including source, destination, conveyances and routes under some risk factors are formulated and solved by the developed MOACOGAD. The model is illustrated numerically. As particular cases three and two dimensional multi-objective imprecise TSPs are derived and solved.
机译：在现实世界中，大多数组合优化问题都是多目标，很难同时优化它们。在文献中，一些单独的算法（ACO，GA等）可用于解决这种离散的多目标优化问题（MOOPS），特别是旅行推销员问题（TSP）。这里，开发了一种结合ACO和GA具有多样性的混合算法，以解决离散的多目标TSP和命名的Moacogad。通常在TSP中，旅行路线不被视为路线的长度保持不变。在现实生活中，可能有几条线路从一个目的地到另一个目的地，这些路线的条件也可能不同，如良好，粗糙，坏等。在实际的情况下，旅行成本和旅行时间不会精确定义，并表示模糊数据。当涉及每单位长度的模糊旅行成本和模糊行程时，路线的长度和条件与用于旅行的运输类型的速度变得重要。在某些情况下，还涉及旅行风险。在本文中，由一些风险因素的源，目的地，销售和路线提供四维不精确的TSP，由发达的摩克托制定和解决。该模型在数字上示出。由于特定情况，衍生和解决三个和二维多目标不精确TSP。

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