Cognitive decision engine design for CR based IoTs using Differential Evolution and Bat Algorithm

2018 ◽  
Author(s):  
Avneet Kaur ◽  
Ashmeet Kaur ◽  
Surbhi Sharma
Keyword(s):  
Differential Evolution ◽  
Bat Algorithm ◽  
Engine Design ◽  
Cognitive Decision ◽  
Decision Engine ◽  
Cognitive Decision Engine

scholarly journals Surrogate modeling based cognitive decision engine for optimization of WLAN performance

2016 ◽  
Vol 23 (8) ◽  
pp. 2347-2359
Author(s):  
David Plets ◽  
Krishnan Chemmangat ◽  
Dirk Deschrijver ◽  
Michael Mehari ◽  
Selvakumar Ulaganathan ◽  
...  
Keyword(s):  
Surrogate Modeling ◽  
Cognitive Decision ◽  
Decision Engine ◽  
Cognitive Decision Engine

scholarly journals The Solution for Optimal Power Flow (OPF) Method Using Differential Evolution Algorithm

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Hazel Ariantara ◽  
Sarjiya Sarjiya ◽  
Sasongko Pramono Hadi
Keyword(s):  
Power Plant ◽  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Differential Evolution Algorithm ◽  
Bat Algorithm ◽  
Plant Production ◽  
Optimal Power ◽  
Bus Voltage ◽  
The Cost

Optimal Power Flow (OPF) is one of techniques used to optimize the cost of power plant production while maintaining the limit of system reliability. In this paper, the application of differential evolution (DE) method is used to solve the OPF problem with variable control such as the power plant output, bus voltage tension, transformer tap, and injection capacitor. The effectiveness of the method was tested using IEEE 30 buses. The result shows that this method is better than generic algorithm (GA), particle swarm optimized (PSO), fuzzy GA, fuzzy PSO, and bat-algorithm. The simulation of the power plant systems of 500 kV Java-Bali with the proposed method can reduce the total cost of generation by 13.04% compared to the operating data PT. PLN (Persero).

scholarly journals Um Estudo Sobre Meta-Heurísticas em Otimização Multimodal

2020 ◽  
Author(s):  
Omar Andres Carmona Cortes ◽  
Lúcio Flávio de Jesus Silva
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Swarm Optimization ◽  
Preliminary Results ◽  
Set Up

RESUMOThis paper presents an investigation of four metaheuristics for multimodaloptimization. The algorithms have been implemented inR and compared against each other using well-known benchmarkfunctions. We implemented the following algorithms: Genetic Algorithm(GA), Particle Swarm Optimization (PSO), Differential Evolution(DE), and Bat Algorithm (BA). For comparisons, we used fivemultimodal benchmarks: Rosenbrock, Griewank, Ackley, Schwefel,and Alpine. Preliminary results show that Bat Algorithm and GeneticAlgorithm tend to discover the best solution considering theparameters that have been set up.

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