Optical packet switching over arbitrary physical topologies using the Manhattan street network : an evolutionary approach

Published in "Towards an Optical Internet", A. Jukan (Ed.). Optical packet switching over arbitrary physical topologies typically mandates complex routing schemes and the use of buffers to resolve the likely contentions. However, the relatively immature nature of optical logic devices and the limitations with optical buffering provide significant incentive to reduce the routing complexity and avoid optical domain contentions. This paper examines how the Manhattan Street Network (MSN) and a particular routing scheme may be used to facilitate optical packet switching over arbitrary physical topologies. A novel approach, genetic algorithms (GA), is applied to the problem of deploying the MSN (near) optimally in arbitrary physical topologies. A problem encoding is proposed and different implementations of GA described. The optimum GA parameters are empirically selected and GA is successfully used to deploy the MSN in physical topologies of up to 100 nodes. Favourable results are obtained. GA are also seen to out-perform other heuristics at deploying the MSN in arbitrary physical topologies for optical packet switching

A self-adaptive genetic algorithm for the flying sidekick travelling salesman problem

This paper presents a novel approach to solving the Flying Sidekick Travelling Salesman Problem (FSTSP) using a state-of-the-art self-adaptive genetic algorithm. The Flying Sidekick Travelling Salesman Problem is a combinatorial optimisation problem that extends the Travelling Salesman Problem (TSP) by introducing the use of drones. In FSTSP, the objective is to minimise the total time to visit all locations while strategically deploying a drone to serve hard-to-reach customer locations. Also, to the best of my knowledge, this is the first time a self-adaptive genetic algorithm (GA) has been used to solve the FSTSP problem. Experimental results on smaller-sized problem instances demonstrate that this algorithm can find a higher quantity of optimal solutions and a lower percentage gap to the optimal solution compared to rival algorithms. Moreover, on larger-sized problem instances, this algorithm outperforms all rival algorithms on each problem size while maintaining a reasonably low computation time.Comment: 17 pages, 9 figure

Multi-Stop Routing Optimization: A Genetic Algorithm Approach

In this research, we investigate and propose new operators to improve Genetic Algorithm’s performance to solve the multi-stop routing problem. In a multi-stop route, a user starts at point x, visits all destinations exactly once, and then return to the same starting point. In this thesis, we are interested in two types of this problem. The first type is when the distance among destinations is fixed. In this case, it is called static traveling salesman problem. The second type is when the cost among destinations is affected by traffic congestion. Thus, the time among destinations changes during the day. In this case, it is called time-dependent traveling salesman problem. This research proposes new improvements on genetic algorithm to solve each of these two optimization problems. First, the Travelling Salesman Problem (TSP) is one of the most important and attractive combinatorial optimization problems. There are many meta-heuristic algorithms that can solve this problem. In this paper, we use a Genetic Algorithm (GA) to solve it. GA uses different operators: selection, crossover, and mutation. Sequential Constructive Crossover (SCX) and Bidirectional Circular Constructive Crossover (BCSCX) are efficient to solve TSP. Here, we propose a modification to these crossovers. The experimental results show that our proposed adjustment is superior to SCX and BCSCX as well as to other conventional crossovers (e.g. Order Crossover (OX), Cycle Crossover (CX), and Partially Mapped Crossover (PMX)) in term of solution quality and convergence speed. Furthermore, the GA solver, that is improved by applying inexpensive local search operators, can produce solutions that have much better quality within reasonable computational time. Second, the Time-Dependent Traveling Salesman Problem (TDTSP) is an interesting problem and has an impact on real-life applications such as a delivery system. In this problem, time among destinations fluctuates during the day due to traffic, weather, accidents, or other events. Thus, it is important to recommend a tour that can save driver’s time and resources. In this research, we propose a Multi-Population Genetic Algorithm (MGA) where each population has different crossovers. We compare the proposed MG against Single-Population Genetic Algorithm (SGA) in terms of tour time solution quality. Our finding is that MGA outperforms SGA. Our method is tested against real-world traffic data [1] where there are 200 different instances with different numbers of destinations. For all tested instances, MGA is superior on average by at least 10% (for instances with size less than 50) and 20% (for instances of size 50) better tour time solution compared to SGA with OX and SGA with PMX operators, and at least 4% better tour time compared toga with SCX operator