scholarly journals Solving Routing Problem Using Improved Camel Herds Algorithm

2020 ◽  
Vol 6 (2) ◽  
pp. 53-59
Author(s):  
Ahad Khaleghi Ardabili ◽  
Zied Othman Ahmed ◽  
Ali Layth Abbood
Keyword(s):  
Heuristic Algorithms ◽  
Routing Algorithm ◽  
Performance Criteria ◽  
Routing Problem ◽  
Switched Networks ◽  
Distributed Routing ◽  
Data Packets ◽  
Mean Relative Error ◽  
Multi Agent ◽  
Error Standard Deviation

This paper introduces a new adaptive, distributed routing algorithm based on the Improved  Camel Herds Algorithm (CHA). It is an intelligent, multi-agent optimization algorithm that is inspired by the behavior of camels and how they search for food in their desert environment. We examine its ability to solve the routing problem in switched networks: finding the shortest path in the process of transferring data packets between networks. Many meta-heuristic algorithms have been previously proposed to address the routing problem, and this proposed approach is compared with three well-known algorithms (ACO, GA, PSO) on ten graphs (weighted, integer, and not negative) and datasets with various size of nodes (from 10 nodes to 297 nodes). Three performance criteria were used to evaluate the performance of the algorithms (mean relative error, standard deviation, and number of function evaluations). The results proved that the performance of the proposed algorithm is both promising and competitive with other algorithms.

Antnet Routing Algorithm with Link Evaporation and Multiple Ant Colonies to Overcome Stagnation Problem

2013 ◽  
pp. 255-274
Author(s):  
Firat Tekiner ◽  
Zabih Ghassemlooy
Keyword(s):  
Routing Algorithm ◽  
Packet Delay ◽  
Average Delay ◽  
Ant Colonies ◽  
Feedback Information ◽  
Switched Networks ◽  
Agent Based ◽  
Data Packets ◽  
Reinforcement Parameter ◽  
Distance Vector

Antnet is a software agent-based routing algorithm that is influenced by the unsophisticated and individual ant’s emergent behaviour. The aim of this chapter is twofold, firstly to introduce improvements to the antnet routing algorithm and then to critically review the work that is done around antnet and reinforcement learning in routing applications. In this chapter a modified antnet algorithm for packet-based networks has been proposed, which offers improvement in the throughput and the average delay by detecting and dropping packets routed through the non-optimal routes. The effect of traffic fluctuations has been limited by applying boundaries to the reinforcement parameter. The round trip feedback information supplied by the software agents is reinforced by updated probability entries in the distance vector table. In addition, link usage information is also used to prevent stagnation problems. Also discussed is antnet with multiple ant colonies applied to packet switched networks. Simulation results show that the average delay experienced by data packets is reduced for evaporation for all cases when non-uniform traffic model traffic is used. However, there is no performance gain on the uniform traffic models. In addition, multiple ant colonies are applied to the packet switched networks, and results are compared with the other approaches. Results show that the throughput could be increased when compared to other schemes, but with no gain in the average packet delay time.

scholarly journals An Optimum Channel Routing Algorithm in the Knock-knee Diagonal Model

VLSI Design ◽  
1994 ◽  
Vol 1 (3) ◽  
pp. 233-242 ◽  
Author(s):  
Xiaoyu Song
Keyword(s):  
Routing Algorithm ◽  
Difficult Problem ◽  
Layout Design ◽  
Channel Routing ◽  
Routing Problem ◽  
Vlsi Layout ◽  
Channel Density ◽  
Optimum Algorithm ◽  
Important Time ◽  
Optimum Channel

Channel routing problem is an important, time consuming and difficult problem in VLSI layout design. In this paper, we consider the two-terminal channel routing problem in a new routing model, called knock-knee diagonal model, where the grid consists of right and left tracks displayed at +45° and –45°. An optimum algorithm is presented, which obtains d + 1 as an upper bound to the channel width, where d is the channel density.

PERMUTATION ROUTING AND SORTING ON THE RECONFIGURABLE MESH

1998 ◽  
Vol 09 (02) ◽  
pp. 199-211
Author(s):  
SANGUTHEVAR RAJASEKARAN ◽  
THEODORE MCKENDALL
Keyword(s):  
Lower Bound ◽  
Randomized Algorithms ◽  
Linear Array ◽  
Order Term ◽  
Routing Algorithm ◽  
Sorting Algorithm ◽  
Routing Problem ◽  
Reconfigurable Mesh ◽  
Permutation Routing ◽  
Time Bounds

In this paper we demonstrate the power of reconfiguration by presenting efficient randomized algorithms for both packet routing and sorting on a reconfigurable mesh connected computer. The run times of these algorithms are better than the best achievable time bounds on a conventional mesh. Many variations of the reconfigurable mesh can be found in the literature. We define yet another variation which we call as Mr. We also make use of the standard PARBUS model. We show that permutation routing problem can be solved on a linear array Mr of size n in [Formula: see text] steps, whereas n-1 is the best possible run time without reconfiguration. A trivial lower bound for routing on Mr will be [Formula: see text]. On the PARBUS linear array, n is a lower bound and hence any standard n-step routing algorithm will be optimal. We also show that permutation routing on an n×n reconfigurable mesh Mr can be done in time n+o(n) using a randomized algorithm or in time 1.25n+o(n) deterministically. In contrast, 2n-2 is the diameter of a conventional mesh and hence routing and sorting will need at least 2n-2 steps on a conventional mesh. A lower bound of [Formula: see text] is in effect for routing on the 2D mesh Mr as well. On the other hand, n is a lower bound for routing on the PARBUS and our algorithms have the same time bounds on the PARBUS as well. Thus our randomized routing algorithm is optimal upto a lower order term. In addition we show that the problem of sorting can be solved in randomized time n+o(n) on Mr as well as on PARBUS. Clearly, this sorting algorithm will be optimal on the PARBUS model. The time bounds of our randomized algorithms hold with high probability.

Multicast Routing Algorithm with QoS Constraints in Mobile Ad Hoc Networks

2014 ◽  
Vol 602-605 ◽  
pp. 3169-3172 ◽  
Author(s):  
Yue Li ◽  
Yin Hui Liu ◽  
Zhong Bao Luo
Keyword(s):  
Ad Hoc ◽  
Multicast Routing ◽  
Routing Algorithm ◽  
Estimation Algorithm ◽  
Bandwidth Estimation ◽  
Routing Problem ◽  
Network Bandwidth ◽  
Hoc Networks ◽  
Multicast Routing Algorithm ◽  
Select Function

Through the study of MANET's QoS multicast routing problem, we propose a heuristic-demand multicast routing algorithm. Algorithm combines the MANET network bandwidth estimation algorithm, redefined the select function, restrictions request packets of flooding algorithm, to ensure fair treatment delay and bandwidth. Simulation results show that the algorithm has the advantage of fewer routing overhead, high success rate.

scholarly journals Electrification of Urban Waste Collection: Introducing a Simulation-Based Methodology for Technical Feasibility, Impact and Cost Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 122 ◽  
Author(s):  
Ricardo Ewert ◽  
Alexander Grahle ◽  
Kai Martins-Turner ◽  
Anne Magdalene Syré ◽  
Kai Nagel ◽  
...  
Keyword(s):  
Open Source ◽  
Waste Collection ◽  
Commercial Vehicles ◽  
Urban Waste ◽  
Technical Feasibility ◽  
Routing Problem ◽  
Agent Based ◽  
Simulation Based ◽  
Multi Agent ◽  
Urban Emissions

Electrification is a potential solution for transport decarbonization and already widely available for individual and public transport. However, the availability of electrified commercial vehicles like waste collection vehicles is still limited, despite their significant contribution to urban emissions. Moreover, there is a lack of clarity whether electric waste collection vehicles can persist in real world conditions and which system design is required. Therefore, we introduce a multi-agent-based simulation methodology to investigate the technical feasibility and evaluate environmental and economic sustainability of an electrified urban waste collection. We present a synthetic model for waste collection demand on a per-link basis, using open available data. The tour planning is solved by an open-source algorithm as a capacitated vehicle routing problem (CVRP). This generates plausible tours which handle the demand. The generated tours are simulated with an open-source transport simulation (MATSim) for both the diesel and the electric waste collection vehicles. To compare the life cycle costs, we analyze the data using total cost of ownership (TCO). Environmental impacts are evaluated based on a Well-to-Wheel approach. We present a comparison of the two propulsion types for the exemplary use case of Berlin. And we are able to generate a suitable planning to handle Berlin’s waste collection demand using battery electric vehicles only. The TCO calculation reveals that the electrification raises the total operator cost by 16–30%, depending on the scenario and the battery size with conservative assumptions. Furthermore, the greenhouse gas emissions (GHG) can be reduced by 60–99%, depending on the carbon footprint of electric power generation.

scholarly journals On the restrictive channel thickness estimation

2007 ◽  
Vol 20 (3) ◽  
pp. 499-506
Author(s):  
Iskandar Karapetyan
Keyword(s):  
Positive Integer ◽  
Heuristic Algorithms ◽  
Clique Number ◽  
Directed Path ◽  
Channel Routing ◽  
Routing Problem ◽  
Thickness Estimation ◽  
Channel Thickness ◽  
Np Complete ◽  
Routing Method

Channel routing is an important phase of physical design of LSI and VLSI chips. The channel routing method was first proposed by Akihiro Hashimoto and James Stevens [1]. The method was extensively studied by many authors and applied to different technologies. At present there are known many effective heuristic algorithms for channel routing. A. LaPaugh [2] proved that the restrictive routing problem is NP-complete. In this paper we prove that for every positive integer k there is a restrictive channel C for which ?(C)>? (HG)+L(VG)+k, where ? (C) is the thickness of the channel, ?(HG) is clique number of the horizontal constraints graph HG and L(VG) is the length of the longest directed path in the vertical constraints graph VG.

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