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An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments
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DOI：10.1080/10106049.2019.1595176.png)
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One of the important issues in ubiquitous geographic information science (GIS) is designing user-centric path finding algorithms to meet user needs. Mostly, in a route planning problem, the user's purpose is optimization of two or more objective functions simultaneously. Thus, the problem is a multi-objective problem. In the present study, having considered multi-objective optimization methods in path finding, we developed an algorithm for solving this problem using an improved multi-objective ant colony optimization (ACO) algorithm. Modifications are introduced for various components of the ant colony metaheuristics; specifically, for those associated with the 'ant decision rule'. The proposed algorithm was tested on the studied network. The results demonstrate that the proposed approach has acceptable settings, repeatability and run time. In addition, one of the important research outputs is a pareto-front which allows the user to select the final path according to the desired priorities.
Keyword:
multi-objective route planning
multi-objective ant colony optimization
user-centric system
ubiquitous GIS
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An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP)一种求解多目标资源分配问题 (MORAP) 的有效蚁群优化算法 (ACO)
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