Cooperative Coevolution With Route Distance Grouping for Large-Scale Capacitated Arc Routing Problems

Abstract

Abstract-In this paper, a divide-and-conquer approach is proposed to solve the Large-Scale Capacitated Arc Routing Problem (LSCARP) more effectively. Instead of considering the problem as a whole, the proposed approach adopts the Cooperative Coevolution (CC) framework to decompose it into smaller ones and solve them separately. An effective decomposition scheme called the Route Distance Grouping (RDG) is developed to decompose the problem. Its merit is twofold: Firstly, it employs the route information of the best-so-far solution, so that the quality of the decomposition is upper bounded by that of the best-sofar solution. Thus, it can keep improving the decomposition by updating the best-so-far solution during the search. Secondly, it defines a distance between routes, based on which the potentially better decompositions can be identified. Therefore, RDG is able to obtain promising decompositions and focus the search on the promising regions of the vast solution space. Experimental studies verified the efficacy of RDG on the instances with a large number of tasks and tight capacity constraints, where it managed to obtain significantly better results than its counterpart without decomposition in a much shorter time. Furthermore, the bestknown solutions of the EGL-G LSCARP instances were much improved.

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

“…The results are compared to those achieved by MAENS*-IIa and MAENS*-IIb included in Table 9. The algorithms considered are MAENS* , MAENS-RDG (Mei et al 2014a), VND (Mei et al 2014b) and ILS-RVND (Martinelli et al 2013). No statistical test was carried out due to the partial availability of the results of the compared algorithms ants that make use of the Proportional Reward (a and b) were tested against the oracle.…”

“…We consider the results of MAENS* , of MAENS-RDG (Mei et al 2014a) and VND (Mei et al 2014b) and an algorithm combining iterate local search and variable neighbourhood descent (Martinelli et al 2013).…”

On investigation of interdependence between sub-problems of the Travelling Thief Problem

“…When tackling large-scale optimization problems, the divideand-conquer approach is commonly adopted to decompose the overall problem into smaller sub-problems (Boyd et al 2007;Omidvar et al 2014;Mei et al 2014a). For many real-world problems, the sub-problems are naturally defined.…”

“…Although there have been extensive studies for solving various forms of the capacitated arc routing problem depending on different practical scenarios [e.g., the classic model (Mei et al 2009a, b;Tang et al 2009;Fu et al 2010), the multi-objective model (Mei et al 2011a), the stochastic model ), the periodic model (Mei et al 2011b) and the large-scale model (Mei et al 2013(Mei et al , 2014a], two important practical issues have been overlooked so far. One is the service profit, which is the profit that can be gained by serving the customers.…”

Variable neighborhood decomposition for Large Scale Capacitated Arc Routing Problem

“…Therefore, it is important to study how to solve LSCARP. The previous studies showed that applying the approaches for CARP directly to LSCARP cannot lead to promising performance [11] [12] [13] [14] [15] [16] due to the scalability issue. That is, the performance of the previous algorithms deteriorates rapidly either in quality [11] [12] or in speed [13] with the increasing problem size.…”