Evaluation and Efficiency Comparison of Evolutionary Algorithms for Service Placement Optimization in Fog Architectures
This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi-Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.
Code (1)
Tasks
DiversityEvolutionary AlgorithmsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS meth…
BenchmarkingGPUNeural Architecture SearchA Comparison-Relationship-Surrogate Evolutionary Algorithm for Multi-Objective Optimization
Evolutionary algorithms often struggle to find well converged (e.g small inverted generational distance on test problems) solutions to multi-objective optimization problems on a limited budget of function evaluations (he…
Evolutionary AlgorithmsCMA-ES with Radial Basis Function Surrogate for Black-Box Optimization
Evolutionary optimization algorithms often face defects and limitations that complicate the evolution processes or even prevent them from reaching the global optimum. A notable constraint pertains to the considerable qua…
Evolutionary AlgorithmsSEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking
We present SEvoBench, a modern C++ framework for evolutionary computation (EC), specifically designed to systematically benchmark evolutionary single-objective optimization algorithms. The framework features modular impl…
BenchmarkingComputational EfficiencyEvolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons
Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has consider…