Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

Mohiuddin, Mohammad; Khan, Salman; Engelbrecht, Andries P.

doi:10.1007/s10489-014-0523-3

Cited by 12 publications

(12 citation statements)

References 47 publications

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“…It would be preferred to indicates that minimizing the number of unused links also affects the performance of the network. This positive effect on the performance is due to traffic distribution across the links of the networks which depends on the routing paths established [42]. Therefore, a new solution might create new routing paths such that traffic on congested links may be distributed on unused links.…”

Section: Open Shortest Path First Weight Setting Problemmentioning

confidence: 99%

“…Since fuzzy evolutionary PSO algorithm (FEPSO) performed better than fuzzy PSO (FPSO) algorithm, it was compared with other iterative heuristics, namely, Pareto-dominance PSO (PDPSO) [64], PSO with weighted aggregation (WAPSO) [65], non-dominated sorting genetic algorithm II (NSGA-II) [66,42], simulated evolution (SimE) [40,42], and simulated annealing (SA) [39,42]. PDPSO and WAPSO were adapted for the underlying problem, whereas NSGA-II, SimE, and SA have already been applied to the same problem by Mohiuddin et al [42].…”

Section: Comparison Of Fuzzy Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

“…PDPSO and WAPSO were adapted for the underlying problem, whereas NSGA-II, SimE, and SA have already been applied to the same problem by Mohiuddin et al [42]. Details of implementation and comparative analysis of NSGA-II, SimE, and SA can be found in Mohiuddin et al [42]. Thirty runs were made for each test cases for each algorithm, and average results and standard deviations were reported.…”

Section: Comparison Of Fuzzy Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

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Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

2016

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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.

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Section: Open Shortest Path First Weight Setting Problemmentioning

confidence: 99%

Section: Comparison Of Fuzzy Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

Section: Comparison Of Fuzzy Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

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Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

2016

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“…Intelligence Optimization Algorithm (such as Genetic Algorithm [1][2][3] (GA), Particle Swarm Optimization [4][5][6][7] (PSO), and Simulated Annealing 8,9 (SA)), which stems from the simulation of biological behavior, physical process, or chemical properties, is an effective method for solving complex optimization problems because it not only solves objective function regardless of continuity conditions but also has simple calculation principle and high-efficiency computing power.…”

Section: Prefacementioning

confidence: 99%

A new specular reflection optimization algorithm

Fan

et al. 2015

Advances in Mechanical Engineering

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With the development of optimization theory and the application of computer technology, some new intellective optimization algorithms are developed quickly and applied widely, which is becoming the most important method for optimization problems. In this article, a new intellective optimization algorithm-Specular Reflection Algorithm-is proposed by authors who are inspired by the physical function of mirror. The traditional mathematical theory is used to prove the global convergence of this new algorithm. In order to validate the performance of this algorithm, three classical testing functions are adopted; then the algorithm is used to solve discrete engineering optimization problems and the results indicate that the Specular Reflection Algorithm has higher computation efficiency and extensive prospect for engineering application.

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“…Yang X et al proposed a BA based on the echolocation behavior of bats, which was applied to solve eight nonlinear engineering optimization problems, and achieved good optimization design results [21]. To solve the engineering combination optimization problems, simulated annealing algorithm (SA), simulating the physical annealing process of solid material, was put forward by S. Kirkpatrick [22,23]. Artificial bee colony (ABC) algorithm was inspired by the intelligent behavior of honey bee swarm to optimize he multivariable functions [24].…”

Section: Introductionmentioning

confidence: 99%

CMSRAS: A Novel Chaotic Multi-Specular Reflection Optimization Algorithm Considering Shared Nodes

Lü

Zhang

et al. 2021

IEEE Access

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Specular reflection algorithm (SRA) was a single population meta-heuristic algorithm inspired by the physical function of mirror. However, similar to most of meta-heuristic algorithms, it had the disadvantages of weak population diversity, stagnation in local optimal and low convergence rate. In order to overcome these shortcomings, a chaotic multi-specular reflection optimization algorithm considering shared nodes (CMSRAS) was proposed by the combination of population strategy with shared node, improved Tent chaos strategy and Gaussian mutation strategy. Initially, a single population SRA was extended to the multi-population with shared node and the population was initialized by improved Tent chaos sequence to improve the quality of the initial solution and the population diversity, and to enhance the global search ability. Meanwhile, to strengthen the local search ability and the convergence accuracy, the Gaussian mutation and improved Tent chaotic disturbance strategy were introduced into SRA. And then the influence law and sensitivity analysis of the CMSRAS algorithm between the initial setting parameters were obtained based on the response surface method and the Sobol's method. Finally, compared with both 12 state-of-the-art algorithms and 8 well-known advanced algorithms, the performance of CMSRAS was evaluated on a comprehensive set of 32 benchmark problems. In addition, CMSRAS was applied to solve the complex optimization problems of engineering structure. The results demonstrated that proposed CMSRAS algorithm outperformed most competitive algorithms and efficiently solve the real-life global optimization problems.

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Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

Cited by 12 publications

References 47 publications

Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

A new specular reflection optimization algorithm

CMSRAS: A Novel Chaotic Multi-Specular Reflection Optimization Algorithm Considering Shared Nodes

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