Metaheuristics for the bi-objective orienteering problem

Abstract

In this paper, heuristic solution techniques for the multi-objective orienteering problem are developed. The motivation stems from the problem of planning individual tourist routes in a city. Each point of interest in a city provides different benefits for different categories (e.g., culture, shopping). Each tourist has different preferences for the different categories when selecting and visiting the points of interests (e.g., museums, churches). Hence, a multi-objective decision situation arises. To determine all the Pareto optimal solutions, two metaheuristic search techniques are developed and applied. We use the Pareto ant colony optimization algorithm and extend the design of the variable neighborhood search method to the multi-objective case. Both methods are hybridized with path relinking procedures. The performances of the two algorithms are tested on several benchmark instances as well as on real world instances from different Austrian regions and the cities of Vienna and Padua. The computational results show that both implemented methods are well performing algorithms to solve the multi-objective orienteering problem.

The Automatic Design of Multiobjective Ant Colony Optimization Algorithms

“…In the problem being solved in the original paper [5], there was no clear definition of heuristic information for each objective, and, hence, the authors used a single heuristic matrix. However, in later publications [31], P-ACO uses multiple heuristic matrices, one for each objective, which are aggregated in the same way as the pheromone matrices. In our earlier work [14], we show that there are important differences between using one or two heuristic matrices in P-ACO for the bi-objective TSP.…”

An experimental analysis of design choices of multi-objective ant colony optimization algorithms

“…In later publications, however, Schilde et al (2009) use multiple heuristic matrices, one for each objective. The heuristic matrices are aggregated in the same way as the pheromone matrices, that is, by means of a weighted sum.…”

Automatic Maintenance Routes Based on the Quality Assurance Information

“…The biobjectivity of the gain function f defined in (12) is taken care of using a similar approach to Schilde et al (2009), where two different pheromones are used for the different objectives. That is, the pheromone additions are done with the similar all-for-the-few-best fashion.…”

“…Literature knows a variety of such strategies (e.g. Yu et al 2011, Schilde et al 2009, Bell and McMullen 2004.…”

“…Schilde et al 2009) and the last one is developed especially for this problem. First, we permutate the stations in the route of each technician to find if the stations can be arranged to lower cost order.…”