Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC

Hybrid bootstrap-based approach with binary artificial bee colony and particle swarm optimization in Taguchi's T-Method

Taguchi's T-Method is one of the Mahalanobis Taguchi System (MTS)-ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. When evaluating data using a system such as the Taguchi's T-Method, bias issues often appear due to inconsistencies induced by model complexity, variations between parameters that are not thoroughly configured, and generalization aspects. In Taguchi's T-Method, the unit space determination is too reliant on the characteristics of the dependent variables with no appropriate procedures designed. Similarly, the least square-proportional coefficient is well known not to be robust to the effect of the outliers, which indirectly affects the accuracy of the weightage of SNR that relies on the model-fit accuracy. The small effect of the outliers in the data analysis may influence the overall performance of the predictive model unless more development is incorporated into the current framework. In this research, the mechanism of improved unit space determination was explicitly designed by implementing the minimum-based error with the leave-one-out method, which was further enhanced by embedding strategies that aim to minimize the impact of variance within each parameter estimator using the leave-one-out bootstrap (LOOB) and 0.632 estimates approaches. The complexity aspect of the prediction model was further enhanced by removing features that did not provide valuable information on the overall prediction. In order to accomplish this, a matrix called Orthogonal Array (OA) was used within the existing Taguchi's T-Method. However, OA's fixed-scheme matrix, as well as its drawback in coping with the high-dimensionality factor, leads to a sub- optimal solution. On the other hand, the usage of SNR, decibel (dB) as its objective function proved to be a reliable measure. The architecture of a Hybrid Binary Artificial Bee Colony and Particle Swarm Optimization (Hybrid Binary ABC-PSO), including the Binary Bitwise ABC (BitABC) and Probability Binary PSO (PBPSO), has been developed as a novel search engine that helps to cater the limitation of OA. The SNR (dB) and mean absolute error (MAE) were the main part of the performance measure used in this research. The generalization aspect was a fundamental addition incorporated into this research to control the effect of overfitting in the analysis. The proposed enhanced parameter estimators with feature selection optimization in this analysis had been tested on 10 case studies and had improved predictive accuracy by an average of 46.21% depending on the cases. The average standard deviation of MAE, which describes the variability impact of the optimized method in all 10 case studies, displayed an improved trend relative to the Taguchi’s T-Method. The need for standardization and a robust approach to outliers is recommended for future research. This study proved that the developed architecture of Hybrid Binary ABC-PSO with Bootstrap and minimum-based error using leave-one-out as the proposed parameter estimators enhanced techniques in the methodology of Taguchi's T-Method by effectively improving its prediction accuracy

Hibrit algoritma kullanarak elektrik enerji tüketim modelinin oluşturulması ve kestirimi : Uganda örneği

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Uzun vadeli elektrik tüketimi tahmini karar vericiler tarafından sistem genişletme planlaması konusunda karar vermek için kullanılır. Geçtiğimiz on yıl boyunca, elektrik tüketim tahminleri üzerine yapılan araştırmaların nokta tahminleri olarak sonuçları rapor edilmiştir. Özellikle uzun vadeli tahminler için nokta tahminleri çok fazla ilgi çekici değildir. Çünkü bunun sistem genişletme ile ilgili finansal riskinin, talep değişkenliğinin ve tahmin belirsizliğinin tahmin edilmesi için kullanılması güçtür. Bu çalışmada ilk olarak, Uganda'nın net elektrik tüketimini modellemek için, tahmin modellerinde nüfusu, gayri safi yurtiçi hasılayı, abone sayısını ve ortalama elektrik fiyatını değişken olarak gözönüne almak suretiyle üstel, karesel ve Adaptif sinirsel bulanık çıkarım sistemi (ANFIS) formları kullanılmıştır. Parçacık Sürüsü Optimizasyonu (PSO) ve Yapay Arı Kolonosi (YAK) algoritmalarına dayalı bir hibrit algoritma kullanılarak üstel ve karesel tahmin modellerinin parametreleri optimize edilmiştir. ANFIS modelinin parametreleri ise, PSO ve Genetik Algoritma (GA) kullanılarak optimize edilmiştir. İkinci olarak, %90 anlamlılık düzeyli alt ve üst hata sınırlarını elde etmek için basit doğrusal regresyonu kullanarak tahmin kalıntıları modellenmiştir. Uganda'nın 2040 yılına kadarki net elektrik tüketimine ilişkin tahmin aralıklarını oluşturmak için alt ve üst hata sınırları kullanılmıştır. Son olarak, birleştirilmiş öngörme modeli elde etmek için bu dört yönteme ilişkin dört model de birleştirilmiştir. Birleştirilmiş tahminlere göre, 2040 yılında Uganda'nın elektrik tüketim tahmininin, yıllık ortalama %11,75 - %10,64'lük bir artışa işaretle [41,296 42,133] GWh arasında olacağı tahmin edilmiştirLong term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power, quadratic and Adaptive Neuro Fuzzy Inference System (ANFIS) forms to model Uganda's net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of power and quadrtaic forecasting models using a hybrid algorithm based on particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. The parameters of ANFIS model are optmized using particle swarm optimization and genetic algorithm. Secondly we model the forecast residuals using simple linear regression to obtain 90% significance level lower and upper error bounds. The lower and upper error bounds were used to construct predication intervals for Uganda's net electricity consumption up to year 2040. Finally we combine all the four models from the two methods to get a combined forecasting model. According to the combined forecast, in year 2040 Uganda's electricity consumption will be between [41,296 42,133] GWh indicating an annual average increase of 11.75%-10.64

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods