Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem

作者：Pahlavani Parham^*; Delavar Mahmoud R; Frank Andrew U

来源：International Journal of Applied Earth Observation and Geoinformation, 2012, 18: 313-328.

DOI：10.1016/j.jag.2012.03.004

• Personalized urban multi-criteria path optimization problem
• Invasive weed optimization algorithm
• Genetic algorithm

The personalized urban multi-criteria quasi-optimum path problem (PUMQPP) is a branch of multi-criteria shortest path problems (MSPPs) and it is classified as a NP-hard problem. To solve the PUMQPP, by considering dependent criteria in route selection, there is a need for approaches that achieve the best compromise of possible solutions/routes. Recently, invasive weed optimization (IWO) algorithm is introduced and used as a novel algorithm to solve many continuous optimization problems. In this study, the modified algorithm of IWO was designed, implemented, evaluated, and compared with the genetic algorithm (GA) to solve the PUMQPP in a directed urban transportation network. In comparison with the GA, the results have shown the significant superiority of the proposed modified IWO algorithm in exploring a discrete search-space of the urban transportation network. In this regard, the proposed modified IWO algorithm has reached better results in fitness function, quality metric and running-time values in comparison with those of the GA.

• 出版日期2012-8