Algorithms for the minimum sum coloring problem: a review

The Minimum Sum Coloring Problem (MSCP) is a variant of the well-known vertex coloring problem which has a number of AI related applications. Due to its theoretical and practical relevance, MSCP attracts increasing attention. The only existing review on the problem dates back to 2004 and mainly covers the history of MSCP and theoretical developments on specific graphs. In recent years, the field has witnessed significant progresses on approximation algorithms and practical solution algorithms. The purpose of this review is to provide a comprehensive inspection of the most recent and representative MSCP algorithms. To be informative, we identify the general framework followed by practical solution algorithms and the key ingredients that make them successful. By classifying the main search strategies and putting forward the critical elements of the reviewed methods, we wish to encourage future development of more powerful methods and motivate new applications

Two genetic algorithms for the bandwidth multicoloring problem

In this paper the Bandwidth Multicoloring Problem (BMCP) and the Bandwidth Coloring Problem (BCP) are considered. The problems are solved by two genetic algorithms (GAs) which use the integer encoding and standard genetic operators adapted to the problems. In both proposed implementations, all individuals are feasible by default, so search is directed into the promising regions. The first proposed method named GA1 is a constructive metaheuristic that construct solution, while the second named GA2 is an improving metaheuristic used to improve an existing solution. Genetic algorithms are tested on the publicly-available GEOM instances from the literature. Proposed GA1 has achieved a much better solution than the calculated upper bound for a given problem, and GA2 has significantly improved the solutions obtained by GA1. The obtained results are also compared with the results of the existing methods for solving BCP and BMCP

Optimizing resource allocation in next-generation optical access networks

To meet rapidly increasing traffic demands caused by the popularization of Internet and the spouting of bandwidth-demanding applications, Passive Optical Networks (PONs) exploit the potential capacities of optical fibers, and are becoming promising future-proof access network technologies. On the other hand, for a broader coverage area and higher data rate, integrated optical and wireless access is becoming a future trend for wireless access. This thesis investigates three next-generation access networks: Time Division Multiplexing (TDM) PONs, Wavelength Division Multiplexing (WDM) PONs, and WDM Radio-Over-Fiber (RoF) Picocellular networks. To address resource allocation problems in these three networks, this thesis first investigates respective characteristics of these networks, and then presents solutions to address respective challenging problems in these networks. In particular, three main problems are addressed: arbitrating time allocation among different applications to guarantee user quality of experience (QoE) in TDM PONs, scheduling wavelengths optimally in WDM PONs, and jointly allocating fiber and radio resources in WDM RoF Picocellular networks. In-depth theoretical analysis and extensive simulations have been performed in evaluating and demonstrating the performances of the proposed schemes

Review of routing and wavelength assignment problem

In today’s internet world there is a growing demand of network bandwidth. Where traditional copper fibers offer very less bandwidth, optical fibers can offer very lager bandwidth. So, there is a growing sense of using optical fibers. Optical networks generally use wavelength division multiplexing (WDM) technique, which is the backbone of future generation internet. In WDM networks fibers are logically divided into non-interfering, circuit-switched communication channels. In optical network Routing and Wavelength Assignment (RWA) problem is a typical problem. This can be seen as a conjunction of two problems, one is Routing and other one is Wavelength Assignment. First one finds a route from source to destination for requested connection and the next one assigns a wavelength to this route. The nature of RWA problem is NP-complete. Hence, heuristic approaches suits well for this class of problems. RWA problem can be formulated as Integer linear programming (ILP) problem. This type of problem focuses on optimizing a single objective. Here objectives may be minimizing the number of amplifiers or maximizing the number of connections or minimizing the number of wavelength used. But our primary objective in RWA problem is to establish a loop free path which minimizes the crosstalk. To achieve this objective we are taking the help of genetic algorithm (GA). Congestion among the individual lightpath request will be the parameter for the application of genetic algorithm