An iterative local search approach based on fitness landscapes analysis for the delay-constrained multicast routing problem

Xu, Ying; Qu, Rong

doi:10.1016/j.comcom.2011.10.011

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Due to the complex real world constraints in multi-objective MRPs, the search space of such problems becomes highly restricted and unpredictable (Xu and Qu 2012). This demands more efficient and effective optimisation techniques to traverse the search space of such problems with many local optimal solutions and disconnected regions of feasible solutions.…”

Section: The Multi-objective Mrpmentioning

confidence: 99%

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

2013

Ann Oper Res

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Abstract. This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing strategies and genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilizations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature.

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Section: The Multi-objective Mrpmentioning

confidence: 99%

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

2013

Ann Oper Res

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“…In bioinspired computing, ant colony optimization (ACO) has been widely used to develop metaheuristic algorithms for combinatorial optimization problems such as asymmetric travelling salesman [12], graph coloring problem [13], and vehicle routing problem [14]. Metaheuristic algorithms such as tabu search [15], simulated annealing [16], iterative local search [17], evolutionary computation [18], and ant colony optimization [19] provide computational methods for heuristic optimization of a problem. These algorithms must be customized according to the application to find the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks

Jabbar

Iram

Imran

et al. 2015

Mathematical Problems in Engineering

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Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications because these networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy in complex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routing algorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizes the routing process through selection of least hop count path with more energy. It consists of three phases, that is, route discovery, forwarding node, and route selection. We have improved the route discovery procedure and mainly concentrate on energy efficient forwarding node and route selection, so that the network lifetime can be prolonged. The four possible cases of forwarding node and route selection are presented. The performance of EASARA is validated through simulation. Simulation results demonstrate the performance supremacy of EASARA over contemporary scheme in terms of various metrics.

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Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

Castro

et al. 2012

J Heuristics

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This paper presents the first investigation on applying a Particle Swarm Optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (2007), and also propose two novel local search heuristics within our PSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our PSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed PSO algorithm over a number of other state-of-the-art approaches.

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An iterative local search approach based on fitness landscapes analysis for the delay-constrained multicast routing problem

Cited by 5 publications

References 43 publications

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

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