1,829 research outputs found
Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem
The open shortest path first (OSPF) routing protocol
is a well-known approach for routing packets from
a source node to a destination node. The protocol assigns
weights (or costs) to the links of a network. These weights
are used to determine the shortest paths between all sources
to all destination nodes. Assignment of these weights to
the links is classified as an NP-hard problem. The aim
behind the solution to the OSPF weight setting problem is
to obtain optimized routing paths to enhance the utilization
of the network. This paper formulates the above problem
as a multi-objective optimization problem. The optimization
metrics are maximum utilization, number of congested
links, and number of unused links. These metrics are conflicting
in nature, which motivates the use of fuzzy logic
to be employed as a tool to aggregate these metrics into a
scalar cost function. This scalar cost function is then optimized
using a fuzzy particle swarm optimization (FPSO)
algorithm developed in this paper. A modified variant of the
proposed PSO, namely, fuzzy evolutionary PSO (FEPSO),
is also developed. FEPSO incorporates the characteristics of the simulated evolution heuristic into FPSO. Experimentation
is done using 12 test cases reported in literature. These
test cases consist of 50 and 100 nodes, with the number of
arcs ranging from 148 to 503. Empirical results have been
obtained and analyzed for different values of FPSO parameters.
Results also suggest that FEPSO outperformed FPSO
in terms of quality of solution by achieving improvements
between 7 and 31 %. Furthermore, comparison of FEPSO
with various other algorithms such as Pareto-dominance
PSO, weighted aggregation PSO, NSGA-II, simulated evolution,
and simulated annealing algorithms revealed that
FEPSO performed better than all of them by achieving best
results for two or all three objectives.http://link.springer.com/journal/104892017-10-31hb2016Computer Scienc
Algorithms based on spider daddy long legs for finding the optimal route in securing mobile ad hoc networks
Mobile ad hoc networks (MANETs) are wireless networks that are subject to severe attacks, such as the black hole attack. One of the goals in the research is to find a method to prevent black hole attacks without decreasing network throughput or
increasing routing overhead. The routing mechanism in define uses route requests (RREQs; for discovering routes) and route replies (RREPs; for receiving paths). However, this mechanism is vulnerable to attacks by malicious black hole nodes. The mechanism is developed to find the shortest secure path and to reduce overhead using
the information that is available in the routing tables as an input to propose a more complex nature-inspired algorithm. The new method is called the Daddy Long-Legs Algorithm (PGO-DLLA), which modifies the standard AODV and optimizes the
routing process. This method avoids dependency exclusively on the hop counts and destination sequence numbers (DSNs) that are exploited by malicious nodes in the standard AODV protocol. The experiment by performance metrics End-to-End delay
and packet delivery ratio are compared in order to determine the best effort traffic. The results showed the PGO-DLLA improvement of the shortest and secure routing from black hole attack in MANET. In addition, the results indicate better performance
than the related works algorithm with respect to all metrics excluding throughput which AntNet is best in routing when the pause time be more than 40 seconds. PGODLLA is able to improve the route discovery against the black hole attacks in AODV.
Experiments in this thesis have shown that PGO-DLLA is able to reduce the normalized routing load, end-to-end delay, and packet loss and has a good throughput and packet delivery ratio when compared with the standard AODV protocol, BAODV protocol, and the current related protocols that enhance the routing security of the AODV protocols
Discrete particle swarm optimization for combinatorial problems with innovative applications.
Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2016.Abstract available in PDF file
Study of Optimization of Tourists' Travel Paths by Several Algorithms
The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PD
A Multi-Objective Mission Planning Method for AUV Target Search
How an autonomous underwater vehicle (AUV) performs fully automated task allocation
and achieves satisfactory mission planning effects during the search for potential threats deployed
in an underwater space is the focus of the paper. First, the task assignment problem is defined
as a traveling salesman problem (TSP) with specific and distinct starting and ending points. Two
competitive and non-commensurable optimization goals, the total sailing distance and the turning
angle generated by an AUV to completely traverse threat points in the planned order, are taken into
account. The maneuverability limitations of an AUV, namely, minimum radius of a turn and speed,
are also introduced as constraints. Then, an improved ant colony optimization (ACO) algorithm
based on fuzzy logic and a dynamic pheromone volatilization rule is developed to solve the TSP.
With the help of the fuzzy set, the ants that have moved along better paths are screened and the
pheromone update is performed only on preferred paths so as to enhance pathfinding guidance in the
early stage of the ACO algorithm. By using the dynamic pheromone volatilization rule, more volatile
pheromones on preferred paths are produced as the number of iterations of the ACO algorithm
increases, thus providing an effective way for the algorithm to escape from a local minimum in
the later stage. Finally, comparative simulations are presented to illustrate the effectiveness and
advantages of the proposed algorithm and the influence of critical parameters is also analyzed
and demonstrated.National Natural Science Foundation of China (NSFC) 52101347Foundations for young scientists' cultivation 7900000
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Traveling Salesman Problem
The idea behind TSP was conceived by Austrian mathematician Karl Menger in mid 1930s who invited the research community to consider a problem from the everyday life from a mathematical point of view. A traveling salesman has to visit exactly once each one of a list of m cities and then return to the home city. He knows the cost of traveling from any city i to any other city j. Thus, which is the tour of least possible cost the salesman can take? In this book the problem of finding algorithmic technique leading to good/optimal solutions for TSP (or for some other strictly related problems) is considered. TSP is a very attractive problem for the research community because it arises as a natural subproblem in many applications concerning the every day life. Indeed, each application, in which an optimal ordering of a number of items has to be chosen in a way that the total cost of a solution is determined by adding up the costs arising from two successively items, can be modelled as a TSP instance. Thus, studying TSP can never be considered as an abstract research with no real importance
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