An improved ant colony optimization-based algorithm for user-centric multi-objective path planning for ubiquitous environments

Abstract 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.

[1]  Oscar Castillo,et al.  Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots , 2006, Soft Comput..

[2]  Alexander Zipf,et al.  Formal definition of a user-adaptive and length-optimal routing graph for complex indoor environments , 2011, Geo spatial Inf. Sci..

[3]  Roberto Montemanni,et al.  Ant colony optimization for real-world vehicle routing problems , 2007, Swarm Intelligence.

[4]  Nicolas Jozefowiez,et al.  Multi-objective vehicle routing problems , 2008, Eur. J. Oper. Res..

[5]  Juan Julián Merelo Guervós,et al.  CHAC. A MOACO Algorithm for Computation of Bi-Criteria Military Unit Path in the Battlefield , 2006 .

[6]  Benjamín Barán,et al.  A Multiobjective Ant Colony System for Vehicle Routing Problem with Time Windows , 2003, Applied Informatics.

[7]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[8]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[9]  Sidan Du,et al.  Multi-objective path finding in stochastic networks using a biogeography-based optimization method , 2016, Simul..

[10]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[11]  S. Kamal Chaharsooghi,et al.  An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP) , 2008, Appl. Math. Comput..

[12]  Qingquan Li,et al.  A multiobjective model for generating optimal landmark sequences in pedestrian navigation applications , 2011, Int. J. Geogr. Inf. Sci..

[13]  Moacir Godinho Filho,et al.  Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research , 2013, Eng. Appl. Artif. Intell..

[14]  Ling Li,et al.  An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night , 2017, Int. J. Geogr. Inf. Sci..

[15]  M.N.S. Swamy,et al.  Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature , 2016 .

[16]  Yaochu Jin,et al.  Multi-Objective Machine Learning , 2006, Studies in Computational Intelligence.

[17]  Li Xiang,et al.  A trajectory-oriented, carriageway-based road network data model, Part 2: Methodology , 2006 .

[18]  Sengul Dogan,et al.  A multi-objective route planning model based on genetic algorithm for cuboid surfaces , 2018 .

[19]  Karl F. Doerner,et al.  Multicriteria tour planning for mobile healthcare facilities in a developing country , 2007, Eur. J. Oper. Res..

[20]  Benjamín Barán,et al.  Reasons of ACO's Success in TSP , 2004, ANTS Workshop.

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  Stephen A. Jarvis,et al.  Road distance and travel time for an improved house price Kriging predictor , 2018, Geo spatial Inf. Sci..

[23]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[24]  Eliseo Clementini,et al.  Data trustworthiness and user reputation as indicators of VGI quality , 2018, Geo spatial Inf. Sci..

[25]  Nicola Bellomo,et al.  ON THE MATHEMATICAL THEORY OF VEHICULAR TRAFFIC FLOW I: FLUID DYNAMIC AND KINETIC MODELLING , 2002 .