A Novel Fast Multi-objective Evolutionary Algorithm for QoS Multicast Routing in MANET

Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN) which its parameters are optimized by particle swarm optimization (PSO) algorithm is proposed as multipath routing algorithm. Link expiration time (LET) between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA) in terms of the path set reliability and number of paths in the set.

Section: Introductionmentioning

confidence: 99%

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Sheikhan¹,

Hemmati²

2012

“…As evolutionary algorithm (EA) can deal simultaneously with a set of possible solutions in a single run, it is especially suitable to solve MOO problems. Many evolutionary multiobjective optimization algorithms have been developed in the last few years, such as evolutionary computation, swarm intelligence [19][20][21]. As a new form of swarm intelligence, PSO has been used to solve MOO problems.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization with Novel Processing Strategy and its Application

Shen¹,

Wang²,

Tao³

2011

The loss of population diversity is one of main reasons which lead standard particle swarm optimization (SPSO) to suffer from the premature convergence when solving complex multimodal problems. In SPSO, the personal experience and sharing experience are processed with a completely random strategy. It is still an unsolved problem whether the completely random processing strategy is good for maintaining the population diversity. To study this problem, this paper presents a correlation PSO model in which a novel correlative strategy is used to process the personal experience and sharing experience. The relational expression between the correlation coefficient and population diversity is developed through theoretical analysis. It is found that the processing strategy with positive linear correlation is helpful to maintain the population diversity. Then a positive linear correlation PSO, PLCPSO, is proposed, where particles adopt the positive linear correlation strategy to process the personal experience and sharing experience. Finally, PLCPSO has been applied to solve single-objective and multi-objective optimization problems. The experimental results show that PLCPSO is a robust effective optimization method for complex optimization problems.

“…In the past few decades, evolutionary algorithms (EAs) [1][2][3][4][5] , such as genetic algorithms, evolutionary programming, and evolutionary strategies, have been successfully applied to real-world optimization problems. The main advantage of these algorithms, relative to most conventional optimization methods (e.g., Newton-based techniques, linear programming, and interior point methods) lies in that they do not apply mathematical assumptions to the optimization problems and have better global search capabilities.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems

Gao¹,

Hailu²

2010

This paper presents an improved particle swarm optimizer (PSO) for solving multimodal optimization problems with problem-specific constraints and mixed variables. The standard PSO is extended by employing a comprehensive learning strategy, different particle updating approaches, and a feasibility-based rule method. The experiment results show the algorithm located the global optima in all tested problems, and even found a better solution than those previously reported in the literature. In some cases, it outperforms other methods in terms of both solution accuracy and computational cost.