Self-Organizing Migrating Algorithm Research Articles

Training artificial neural networks using self-organizing migrating algorithm for skin segmentation.

This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.

Novel Image Denoising Techniques Using AFMF

Background: In this paper, we have proposed a new image-denoising approach, which is a hybrid technique using the self-organizing migration algorithm (SOMA) and adaptive frequency median filter (AFMF). Materials and Methods: The first step in this approach consists of applying (AFMF) to the noisy image in order to have the first version of the denoised image. This first version of the denoised image is considered a clean image, which is then used as an input of an image-denoising system based on SOMA. This denoising system is then applied for denoising the noisy image and then a final version of the denoised image can be obtained. This image denoising system based on SOMA has two inputs, which are the noisy image and the corresponding clean image. However, we have available only the noisy image, and for that reason, we have first applied the AFMF to the noisy image and then obtained the first version of the denoised image as the clean image. In order to improve this proposed denoising technique, we have replaced the denoising system based on SOMA with targeted image denoising (TID), which is a more recent denoising approach. The PSNR (peak-SNR) and SSIM (structural similarity) have been used for evaluating the performance of the image-denoising techniques proposed in this work. Results: The results obtained from the computations of PSNR and SSIM show the performance of these proposed image-denoising techniques. Conclusion: The results obtained from the computations of PSNR and SSIM show that the proposed image-denoising techniques outperform a number of image-denoising approaches existing in the literature and used here for our evaluation.

Minimize Total Cost and Maximize Total Profit for Power Systems with Pumped Storage Hydro and Renewable Power Plants Using Improved Self-Organizing Migration Algorithm

This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF) for hydrothermal power systems (HTPS) without and with renewable energies. Two power system configurations were employed to test the real efficiency of ISOMA while dealing with two objective functions. In the first configuration, there was one thermal power plant and one hydropower plant, while in the second configuration, wind and solar energy were both connected to the first system. The results achieved in the first configuration with the first objective function indicated that ISOMA not only outperformed SOMA according to all comparison criteria but was also superior to other methods such as evolutionary programming (EP), acceleration factor-based particle swarm optimization (AFPSO), and accelerated particle swarm optimization (APSO). The evaluation of the results achieved by ISOMA in the second configuration with the objective function of maximizing the TPRF revealed that ISOMA could reach better profits than SOMA in terms of maximum, mean and minimum TPRF values over fifty trial runs. As a result, it was concluded that pumped storage hydropower plants are very useful in integrating with renewable power plants to cut total cost for thermal power plants and in reaching the highest profit for the whole system. Also, ISOMA is a suitable algorithm for the considered problem.

Research on the Prediction Method of Conducted Interference in Flyback Converters based on the High-frequency Transformer Model

Conducted electromagnetic interference (EMI) has always been a challenge for designers of switched-mode power supplies. Flyback converters are used in various applications. However, as the switching frequency of these converters increases, the issue of electromagnetic interference becomes progressively more severe. In light of this, this paper presents a predictive method for conducted interference in flyback converters, based on a high-frequency transformer model. A high-frequency transformer model topology is proposed, integrating traditional inductance models with a three-capacitor model. Subsequently, a self-organizing migrating algorithm (SOMA) is employed for the extraction of parameters from the high-frequency transformer model, and a high-frequency model is established for a transformer. Finally, the high-frequency model is applied to the prediction of conducted interference in flyback converters. The results demonstrate that the proposed predictive method can effectively forecast the actual conducted interference, thereby providing a reference for suppression of conducted electromagnetic interference.

Enhancing Software Effort Estimation With Self-Organizing Migration Algorithm: A Comparative Analysis of COCOMO Models

Enhancing Software Effort Estimation With Self-Organizing Migration Algorithm: A Comparative Analysis of COCOMO Models

An Improved Self-Organizing Migration Algorithm for Short-Term Load Forecasting with LSTM Structure Optimization

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM.

Self-organizing migrating algorithm: review, improvements and comparison

The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms.

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.

Application of the modified self-organizing migration algorithm MSOMA in optimal open-loop control problems of switching systems

The article discusses the application of the metaheuristic algorithm of global constrained optimization for solving the problem of finding the optimal open-loop control for nonlinear switching deterministic dynamical systems. The quality of control is assessed by the value of the functional defined on individual trajectories. The optimal control problem is reduced to a parametric optimization problem, which is solved using theMSOMA algorithm, which belongs to the evolutionary group. The MSOMA algorithm is a new algorithm based on the SOMA self-organizing migration algorithm. The modification consists in identifying three leaders among the individuals forming the current population. For each member of the population, two clones are generated with the same position. Thus, in fact, three populations are generated, each of which then realizes a migration cycle (evolutionary process) relative to one of the three selected leaders. A step-by-step algorithm for piecewise-constant, piecewiselinear, quadratic spline and cubic spline methods of control laws approximation is proposed. The effectiveness of the proposed method is demonstrated by the example of solving optimal control problems for switching systems with two and three subsystems. The influence of the parameters of the MSOMA algorithm on the quality of the obtained result is investigated. Comparison of the operation of the algorithm with the known solution is carried out.

Analytic Programming – a Novel Tool for Synthesis of Controller for Chaotic Lozi Map

In this paper, it is presented a utilization of a novel tool for symbolic regression, which is analytic programming, for the purpose of the synthesis of a new feedback control law. This new synthesized chaotic controller secures the fully stabilization of selected discrete chaotic systems, which is the two-dimensional Lozi map. The paper consists of the descriptions of analytic programming as well as selected chaotic system, used heuristic and cost function design. For experimentation, Self-Organizing Migrating Algorithm (SOMA) and Differential evolution (DE) were used. Two selected experiments are detailed described.

Application of the modified self-organizing migration algorithm MSOMA in optimal open-loop control problems

The article discusses the application of the metaheuristic algorithm of global constrained optimization for solving the problem of finding the optimal open-loop control for nonlinear continuous deterministic dynamical systems. The quality of control is assessed by the value of the functional defined on individual trajectories. The optimal control problem is reduced to a parametric optimization problem, which is solved using the MSOMA algorithm, which belongs to the evolutionary group. The MSOMA algorithm is a new algorithm based on the SOMA self-organizing migration algorithm. A step-by-step algorithm for piecewise-constant, piecewise-linear, quadratic spline and cubic spline methods of control laws approximation is proposed. The effectiveness of proposed method is demonstrated by the example of solving the problem of optimal control of a chemical process in a mixing reactor and a singular problem of optimal control. The influence of the parameters of the MSOMA algorithm on the quality of the obtained result is investigated. Comparison of the operation of the algorithm with a known solution, as well as with a solution using the original SOMA method is carried out.

Predicting the impact of transient voltage suppressor used for ESD protection on the low noise amplifier

Low noise amplifiers (LNAs) are highly susceptible to electro-static discharge (ESD) events as they are directly connected to the antennas, therefore transient voltage suppressor (TVS) are often used as protective devices for ESD. In the radio frequency (RF) band, the parasitic parameters of TVS can heavily influence the performance of the functional circuit. The input impedance of the LNA can be disturbed and hence a loss of gain and work performance is the result. In this paper, a novel model of TVS is proposed to predict the impact of TVS on the LNA, which can illustrate the performance of TVS under the no-ESD-occurrence or ESD-occurrence state. The parasitic parameters of TVS is extracted by self-organizing migrating algorithm (SOMA). The gain of LNA with TVS is studied under no-ESD-occurrence state and the ESD protection characteristics of TVS are analyzed under ESD-occurrence state, which can provide a reference for the performance estimation and compensation of the LNA or other electrical equipment.

A new hybrid algorithm to solve bound-constrained nonlinear optimization problems

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been compared numerically and graphically with existing algorithms. In the graphical comparison, a modified performance index has been proposed. Finally, the proposed algorithm has been applied to solve two real-life problems.

Modelling and Parameters Extraction of Flexible Amorphous Silicon Solar Cell a-Si:H

The precise of solar cell model parameters being the prerequisite for realizing accurate photovoltaic models. Hence, the parameters identification techniques have attracted immense interest over the years among the researchers. This paper proposes a modelling and prediction of electrical intrinsic parameter extraction method of flexible hydrogenated amorphous silicon a-Si:H solar cell, based on the meta-heuristic firefly algorithm (FA). The characteristics of solar cells are non-linear, multivariable and multi-modal and difficult to identifies the electrical intrinsic parameters by conventional and analytical methods with high accuracy. Recently, the firefly algorithm has attracted the attention to optimize the non-linear and complex systems, based on the flashing patterns and behaviour of firefly’s swarm. Besides, the proposed constrained objective function is derived from the current–voltage curve. It is the absolute errors between the experimental and calculated current and voltage values. Furthermore, the obtained results of the proposed algorithm are compared with the results obtained by quasi-Newton method (Q-N) and self-organizing migrating algorithm (SOMA). Indeed, to validate the performance of the algorithm, the statistical analyses are carried out to measure the accuracy of the estimated parameters. In the end, the theoretical results of the firefly algorithm show an excellent agreement with experimental data and more accurate compared to other compared techniques.

Разработка модифицированного самоорганизующегося миграционного алгоритма оптимизации (MSOMA)

The modified self-organizing migration optimization algorithm (MSOMA) based on a self-organizing migration algorithm (SOMA) is suggested. An algorithm for solving the problem of finding the global conditional extremum of the objective function on a given set is developed. Examples illustrating the application of the algorithm and created software are given.

Discrete self-organizing migration algorithm and p-location problems

Mathematical modelling, and integer programming generally, has many practical applications in different areas of human life. Effective and fast solving approaches for various optimization problems play an important role in the decision-making process and therefore, big attention is paid to the development of many exact and approximate algorithms. This paper deals only with a special class of location problems in which given number of facilities are to be chosen to minimize the objective function value. Since the exact methods are not suitable for their unpredictable computational time or memory demands, we focus here on possible usage of a special type of a particle swarm optimization algorithm transformed by discretization and meme usage into so-called discrete self-organizing migrating algorithm. In the paper, there is confirmed that it is possible to suggest a sophisticated heuristic for zero-one programming problem, which can produce near-to-optimal solution in much smaller time than the time demanded by exact methods. We introduce a special adaptation of the discrete self-organizing migration algorithm to the $p$-location problem making use of the path-relinking method. In the theoretical part of this paper, we introduce several strategies of the migration process. To verify their features and effectiveness, a computational study with real-sized benchmarks was performed. The main goal of the experiments was to find the most efficient version of the suggested solving tool.

Nonlinear Model Predictive Control for pH Neutralization Process based on SOMA Algorithm

In this work, the pH neutralization process is described by a neural network Wiener (NNW) model. A nonlinear Model Predictive Control (NMPC) is established for the considered process. The main difficulty that can be encountered in NMPC is solving the optimization problem at each sampling time to determine an optimal solution in finite time. The aim of this paper is the use of global optimization method to solve the NMPC minimization problem. Therefore, we propose in this work, to use the Self Organizing Migrating Algorithm (SOMA) to solve the presented optimization problem. This algorithm proves its efficiency to determine the optimal control sequence with a lower computation time. Then the NMPC is compared to adaptive PID controller, where we propose to use the SOMA algorithm to formulate the PID in order to determine the optimal parameters of the PID. The performances of the two controllers based on the SOMA algorithm are tested on the pH neutralization process.

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed.

Modified Nelder-Mead self organizing migrating algorithm for function optimization and its application

In this paper, a modified Nelder Mead Self Organizing Migrating Algorithm (mNM-SOMA) has been presented for solving unconstrained optimization problems. It is based on the hybridization of self organizing migrating algorithm (SOMA) with modified Nelder Mead (mNM) Crossover Operator. SOMA is a low population based technique that has good exploration and exploitation qualities, but sometimes converges premature to local optima solution due to lack of diversity preserve mechanism. In this paper an attempt has been made to improve the efficiency of SOMA using a modified NM crossover operator (mNM) for maintaining the diversity in the search space. mNM-SOMA has been tested on a set of 15 test problems, taken form literature and results are compared with the results obtained by self organizing migrating genetic algorithm (SOMGA), SOMA, genetic algorithm (GA) and particle swarm optimization (PSO). For better presentation, results are also analyzed graphically using a Performance Index. Besides this, mNM-SOMA has also been used to solve Frequency Modulation Sounds Parameter Identification Problem. Analysis of numerical results infers mNM-SOMA as a less expensive robust technique.

Multiparameter estimation in nonhomogeneous participating slab by using self-organizing migrating algorithms

The self-organizing migrating algorithm (SOMA) and improved SOMA with random mutation (RM-SOMA) were introduced to solve the inverse transient radiation problem. An improved SOMA with random searching (RS-SOMA) was developed on the basis of RM-SOMA by generating a random particle in the searching domain. The time-resolved transmittance and reflectance simulated by the finite-volume method were used as measurement data to estimate the absorption coefficient, scattering coefficient, and geometric positions of the interlayer in a three-layered participating slab exposed to the ultrashort pulse laser by the inverse simulation. The sensitivity of the objective function with respect to the absorption coefficient, scattering coefficient, and geometric positions of the interlayer was also investigated. A comparison among three SOMA methods, i.e., the standard SOMA, RM-SOMA, and RS-SOMA, is presented to illustrate the retrieval performance and accuracy. The effect of measurement errors on the accuracy of estimations by inverse analysis is also examined. All results confirm the potential of the proposed RS-SOMA approach and show its effectiveness and superiority over the other two SOMA algorithms. Unknown radiative parameters can be estimated accurately with RS-SOMA even with noisy data. Furthermore, the absorption coefficient and reduced scattering coefficient are retrieved simultaneously with RS-SOMA by measuring the time-resolved transmittance and reflectance signals of the standard solid imitations exposed to ultrashort pulse laser on the basis of the time-correlated single-photon counting technique. Experimental results show that the parameters can be reasonably estimated by RS-SOMA. In conclusion, the improved RS-SOMA is proven effective and robust. Thus, this method has the potential to be implemented in various fields of inverse radiation problems.