A performance comparison of multi-objective optimization evolutionary algorithms for all-optical networks design

Araújo, Danilo R. B.; Bastos-Filho, Carmelo J. A.; Barboza, Erick A.; Chaves, Daniel A. R.; Martins-Filho, Joaquim F.

doi:10.1109/smdcm.2011.5949265

Cited by 23 publications

(17 citation statements)

References 16 publications

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“…SPEA 2 and NSGA-II have been implemented to solve a lot of problems in the Multiobjective and Combinatorial Optimization fiel. For instance, problems such as Pattern-recognition based Machine Translation System (Sofianopoulos & Tambouratzis, 2011), Tuning of Fuzzy Logic controllers for a heating (Gacto et al, 2011), Real-coded Quantum Clones (Xiawen & Yu, 2011), Optimization Problems with Correlated Objectives (Ishibuchi et al, 2011), Production Planning , Optical and Dynamic Networks Designs (Araujo et al, 2011;Wismans et al, 2011), Benchmark multi-objective …”

Section: Tabu Searchmentioning

confidence: 99%

Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

Niño-Ruiz¹

2012

Real-World Applications of Genetic Algorithms

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Section: Tabu Searchmentioning

confidence: 99%

Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

Niño-Ruiz¹

2012

Real-World Applications of Genetic Algorithms

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“…We used the cost model presented in [1], which evaluates the cost in monetary units (m.u.) and considers four different sources: a fixed cost for each wavelength used in the entire network…”

Section: B Capex Modelmentioning

confidence: 99%

“…However, one should observe that the probability to determine the connection between a link is solely 1 |AM P +1| , for k < K + 1, and the probability to choose an specific amplifier among all types of amplifiers is the same. This procedure was used in some previous works to solve the OpNTD problem [1], [2], [5], but the probability to disconnect two nodes is too low. As a consequence, this approach tends to generate highly connected networks.…”

Section: B Capex Modelmentioning

confidence: 99%

“…Some multi-objective evolutionary algorithms (MOEA) that use the dominance concept were already applied to solve the OpNTD problem considering QoS and CAPEX [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Araujo et al [1] demonstrated that the NSGA II (nondominated sorting genetic algorithm II) is the most suitable MOEA among five different options to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

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An efficient multi-objective evolutionary optimizer to design all-optical networks considering physical impairments and CAPEX

Araújo

Bastos-Filho

Barboza

et al. 2011

2011 11th International Conference on Intelligent Systems Design and Applications

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In this paper we propose efficient operators for a well known multi-objective evolutionary optimizer, called NSGA II, applied to design all-optical networks regarding the network topology and the device specifications in order to both minimize the capital expenditure to build the network and to maximize the overall network performance. From the experiments, we perceived that it is better to use an uniform crossover, to include preferences a priori and to initialize the individuals emphasizing the network topology.

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Optimal scheduling workflows in cloud computing environment using Pareto‐based Grey Wolf Optimizer

Khalili

Babamir

2017

Concurrency and Computation

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A workflow consists of dependent tasks, and scheduling of a workflow in a cloud environment means the arrangement of tasks of the workflow on virtual machines (VMs) of the cloud. By increasing VMs and the diversity of task size, we have a huge number of such arrangements.Finding an arrangement with minimum completion time among all of the arrangements is an Non-Polynomial-hard problem. Moreover, the problem becomes more complex when a scheduling should consider a couple of conflicting objectives. Therefore, the heuristic algorithms have been paid attention to figure out an optimal scheduling. This means that although the singleobjective optimization, ie, minimizing completion time, proposes the workflow scheduling as an NP-complete problem, multiobjective optimization for the scheduling problem is confronted with a more permutation space because an optimal trade-off between the conflicting objectives is needed. To this end, we extended a recent heuristic algorithm called Grey Wolf Optimizer (GWO) and considered dependency graph of workflow tasks. Our experiment was carried out using the WorkflowSim simulator, and the results were compared with those of 2 other heuristic task scheduling algorithms.to optimize some conflicting objectives stated previously in the cloud computing environment. We called it Pareto-based GWO (PGWO) and evaluated its performance on 2 patterns of workflows.Similar to the PSO algorithm, the GWO algorithm is based on swarm intelligence proposed by Mirjalili et al. 6 To evaluate the performance of the proposed algorithm practically, we implemented the extend algorithm using the WorkflowSim tool, 7 which is based on the CloudSim tool. Results of the proposed algorithm were compared with those of Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithm. 8The rest of the article is organized as follows. In Section 2, related work is presented in the greedy, single, and multiobjectives categories.In Section 3, first we introduce the concept of task scheduling and then we present our method in 2 parts, the extension of (1) the single-objective optimization to multiobjective optimization (MOO) and (2) the GWO algorithm for support of MOO. In Section 4, experimental results, simulation environment, performance indices, and results of the proposed algorithm are evaluated. Finally, conclusions are drawn in Section 5.

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A performance comparison of multi-objective optimization evolutionary algorithms for all-optical networks design

Cited by 23 publications

References 16 publications

Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

Evolutionary Algorithms Based on the Automata Theory for the Multi-Objective Optimization of Combinatorial Problems

An efficient multi-objective evolutionary optimizer to design all-optical networks considering physical impairments and CAPEX

Optimal scheduling workflows in cloud computing environment using Pareto‐based Grey Wolf Optimizer

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