• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.19-27
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• 2009

This paper presents a newly developed optimization algorithm, the Bee Optimization Algorithm (BeeOA), to solve the economic power dispatch (EPD) problem. The authors have developed a derivative free and global optimization technique based on the working of the honey bee. The economic power dispatch problem is a nonlinear constrained optimization problem. Classical optimization techniques fail to provide a global solution and evolutionary algorithms provide only a good enough solution. The proposed approach has been examined and tested on two test systems with different objectives. A simple power dispatch problem is tested first on 6 generators and then the algorithm is demonstrated on 13 thermal unit systems whose incremental fuel cost function takes into account the value point loading effect. The results are promising and show the effectiveness and robustness of the proposed approach over recently reported methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2574-2587
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• 2019

Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

• Steel and Composite Structures
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• v.12 no.6
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• pp.505-522
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• 2012

An artificial bee colony (ABC) algorithm is developed for the optimum design of geometrically non-linear steel frames. The ABC is a new swarm intelligence method which simulates the intelligent foraging behaviour of honeybee swarm for solving the optimization problems. Minimum weight design of steel frames is aimed under the strength, displacement and size constraints. The geometric non-linearity of the frame members is taken into account in the optimum design algorithm. The performance of the ABC algorithm is tested on three steel frames taken from literature. The results obtained from the design examples demonstrate that the ABC algorithm could find better designs than other meta-heuristic optimization algorithms in shorter time.

• Journal of Power Electronics
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• v.14 no.2
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• pp.362-368
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• 2014

This paper proposes a harmonic reduction approach for a pulse width modulation (PWM) AC-AC converters using Bee Colony Optimization (BCO). The optimal switching angles are provided by BCO to minimize harmonic distortions. The sequences of the PWM switching angles are considered as a technical constraint. In this paper, simulation results from various optimization techniques including BCO, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are compared. The test results indicate that BCO can provide a better solution than the others in terms of power quality and power factor improvement. Lastly, experiments on a 200W AC-AC converter confirm the performance of the proposed switching pattern in reducing harmonic distortions of the output waveform.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.606-609
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• 2015

본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.45-53
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• 2010

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, MTS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

• Advances in aircraft and spacecraft science
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• v.3 no.2
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• pp.149-170
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• 2016

Multi-Disciplinary Optimization (MDO) is widely used to handle the advanced design in several engineering applications. Such applications are commonly simulation-based, in order to capture the physics of the phenomena under study. This framework demands fast optimization algorithms as well as trustworthy numerical analyses, and a synergic integration between the two is required to obtain an efficient design process. In order to meet these needs, an adaptive Computational Fluid Dynamics (CFD) solver and a fast optimization algorithm have been developed and combined by the authors. The CFD solver is based on a high-order discontinuous Galerkin discretization while the optimization algorithm is a high-performance version of the Artificial Bee Colony method. In this work, they are used to address a typical aero-mechanical problem encountered in turbomachinery design. Interesting achievements in the considered test case are illustrated, highlighting the potential applicability of the proposed approach to other engineering problems.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.