Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

WOS: 000576687400001Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. in this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. the adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields

Parameter Tuning in Modeling and Simulations by Using Swarm Intelligence Optimization Algorithms

9th International Conference on Computational Intelligence and Communication Networks (CICN) -- SEP 16-17, 2017 -- Final Int Univ, Girne, CYPRUSWOS: 000432249700032Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results.. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.MIR Labs, IEEE Turkey Sec

Modifiye Yapay Ari Kolonileri Algoritmasi ile Parametre Ayarlama]

Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780Analytical solutions are not possible due to the complexity and large-scale data sets of complex systems. In order to facilitate the examination of these systems, agent based modeling and simulation techniques are often used. When studies done in recent years are examined, it is seen that meta-heuristic algorithms are often used for optimization with modeling and simulation. In this study, Artificial Bee Colonies algorithm is investigated for meta-heuristic algorithms for parameter calibration in complex systems with an optimization problem. The success of the parameter calibration process of complex systems has been tested with the Modified Artificial Bee Colonies Algorithm, which has been proven in this work. © 2018 IEEE

STM32F429 Discovery Board-Based Emulator for Lotka-Volterra Equations

Lotka-Volterra equations are commonly used in prey-predator population studies. Simulation programs are commonly used to produce solutions of Lotka-Volterra equations and to examine their initial value dependendence. In literature, chaotic waveform generators, ECG and EEG generators have been made and used for research and education. To the best of our knowledge, such an electrical circuit to produce the Lotka-Volterra waveforms does not exist. Such a circuit can be made using either analog or digital circuit components. However, such a device may be used for education in classroom and also to prove concepts by population researchers. In this study, implementation and experimental verification of the microcontroller-based circuit which solves Lotka Volterra equations in real time and produces its waveforms are presented. Euler method is used to solve the equation system in discrete time. Presented design has been implemented using an STM32F429 Discovery Board, two DACs and four opamps. The microcontroller sends the signals to the outputs of the circuit using digital-to-analog converters and opamps. The waveforms acquired experimentally from the implemented circuit outputs matches well with those obtained from numerical simulations