• Geomechanics and Engineering
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• 제31권2호
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• 한국산학기술학회논문지
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• 제17권4호
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• pp.1-12
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• 2016

최적화 분야에서 Harmony Search Algorithm (HSA)은 근래에 개발된 메타휴리스틱 최적화 알고리즘의 하나로, HSA가 개발된 이래 공학, 자연과학, 의약학 등 다양한 분야에서 많은 연구자들에 의해 활용되어왔다. 현재 최적화 대상 문제들의 복잡성이 날로 증가하고 있으며 이에 따라 기존 최적화 기법에 대한 효율을 개선하는 방법론 개발에 대한 필요성이 대두되고 있다. 따라서 본 연구에서는 HSA의 구조적 특성에 초점을 맞추어 해탐색 능력을 향상시키는 것을 목표로 하여 새로운 메타휴리스틱 최적화 알고리즘인 Multi-layered Harmony Search Algorithm (MLHSA)을 제안하였다. 개발된 MLHSA는 기존 HSA에 추가적으로 구조적인 특성을 부여하여 전역 탐색 및 지역 탐색 성능을 개선하였다. 또한, 제안된 기법의 효율성과 적용성을 검증하기 위해 이전 최적화 알고리즘 관련 문헌에서 다양한 알고리즘이 적용된 바 있는 수학적 최적해 탐색 문제와 상수도 관망의 최적 관경 설계에 MLHSA를 통한 최적화를 수행하였다. 적용 결과 본 연구에서 개발된 MLHSA는 기존 알고리즘을 효과적으로 대체할 수 있는 최적화 기법임을 확인할 수 있었다.

• Transactions on Electrical and Electronic Materials
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• 제18권6호
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• pp.345-349
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• 2017

Reconfiguration is an important method to minimize power loss and load interruption by creating an optimal configuration of a system. Furthermore, by increasing demand and value of consumption, construction of new power plants can be postponed in networks by reconfiguration and proper arrangement of linkage switches. This method is feasible for radial networks, which create meshes of linkage switches. One convenient way to achieve a system with minimal power loss and interruption is to utilize capacitors. Optimal placement and sizing of capacitors in such applications is an important issue in the literature. In this paper, cat swarm optimization is introduced as a new metaheuristic algorithm to achieve this purpose. Simulation has been carried out in two feasible networks, 69-bus and 33-bus systems.

• ETRI Journal
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• 제39권6호
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• pp.782-793
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• 2017

This paper considers the use of the Partial Transmit Sequence (PTS) technique to reduce the Peak-to-Average Power Ratio (PAPR) of an Orthogonal Frequency Division Multiplexing signal in wireless communication systems. Search complexity is very high in the traditional PTS scheme because it involves an extensive random search over all combinations of allowed phase vectors, and it increases exponentially with the number of phase vectors. In this paper, a suboptimal metaheuristic algorithm for phase optimization based on an improved harmony search (IHS) is applied to explore the optimal combination of phase vectors that provides improved performance compared with existing evolutionary algorithms such as the harmony search algorithm and firefly algorithm. IHS enhances the accuracy and convergence rate of the conventional algorithms with very few parameters to adjust. Simulation results show that an improved harmony search-based PTS algorithm can achieve a significant reduction in PAPR using a simple network structure compared with conventional algorithms.

• Earthquakes and Structures
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• 제12권4호
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• pp.389-395
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• 2017

In this paper a simple approach is presented for spectral matching of ground motions utilizing the wavelet transform and a recently developed metaheuristic optimization technique. For this purpose, wavelet transform is used to decompose the original ground motions to several levels, where each level covers a special range of frequency, and then each level is multiplied by a variable. Subsequently, the vibrating particles system (VPS) algorithm is employed to calculate the variables such that the error between the response and target spectra is minimized. The application of the proposed method is illustrated through modifying 12 sets of ground motions. The results achieved by this method demonstrate its capability in solving the problem. The outcomes of the VPS algorithm are compared to those of the standard colliding bodies optimization (CBO) to illustrate the importance of the enhancement of the algorithm.

• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.310-330
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• 2012

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In manufacturing systems, the purpose of scheduling is to minimize the production time and costs, by assigning a production facility when to make, with which staff, and on which equipment. Production scheduling aims to maximize the efficiency of the operation and reduce the costs. In order to find an optimal solution to manufacturing scheduling problems, it attempts to solve complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. Genetic algorithm (GA) is one of the generic population-based metaheuristic optimization algorithms and the best one for finding a satisfactory solution in an acceptable time for the NP-hard scheduling problems. GA is the most popular type of evolutionary algorithm. In this survey paper, we address firstly multiobjective hybrid GA combined with adaptive fuzzy logic controller which gives fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and four crucial issues in the manufacturing scheduling including a mathematical model, GA-based solution method and case study in flexible job-shop scheduling problem (fJSP), automatic guided vehicle (AGV) dispatching models in flexible manufacturing system (FMS) combined with priority-based GA, recent advanced planning and scheduling (APS) models and integrated systems for manufacturing.

• Steel and Composite Structures
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• 제47권3호
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• pp.339-353
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• 2023

The primary objective of the current study is to optimize and evaluate the seismic performance of steel momentresisting frame (MRF) structures considering soil-structure interaction (SSI) effects. The structural optimization is implemented in the context of performance-based design in accordance with FEMA-350 at different confidence levels from 50% to 90% by taking into account fixed- and flexible-base conditions using an efficient metaheuristic algorithm. Nonlinear response-history analysis (NRHA) is conducted to evaluate the seismic response of structures, and the beam-on-nonlinear Winkler foundation (BNWF) model is used to simulate the soil-foundation interaction under the MRFs. The seismic performance of optimally designed fixed- and flexible-base steel MRFs are compared in terms of overall damage index, seismic collapse safety, and interstory drift ratios at different performance levels. Two illustrative examples of 6- and 12-story steel MRFs are presented. The results show that the consideration of SSI in the optimization process of 6- and 12-story steel MRFs results in an increase of 1.0 to 9.0 % and 0.5 to 5.0 % in structural weight and a slight decrease in structural seismic safety at different confidence levels.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.337-348
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• 2023

The optimization of reinforced concrete (RC) cantilever retaining walls is a complex problem and requires the use of advanced techniques like metaheuristic algorithms. For this purpose, an optimization model must first be developed, which involves mathematical complications, multidisciplinary knowledge, and programming skills. This task has proven to be too arduous and has halted the mainstream acceptance of optimization. Therefore, it is necessary to unravel the complications of optimization into an easily applicable form. Currently, the most commonly used method for designing retaining walls is by following the proportioning limits provided by the ACI handbook. However, these limits, derived manually, are not verified by any optimization technique. There is a need to validate or modify these limits, using optimization algorithms to consider them as optimal limits. Therefore, this study aims to propose updated proportioning limits for the economical design of a RC cantilever retaining wall through a comprehensive parametric investigation using the genetic algorithm (GA). Multiple simulations are run to examine various design parameters, and trends are drawn to determine effective ranges. The optimal limits are derived for 5 geometric and 3 reinforcement variables and validated by comparison with their predecessor, ACI's preliminary proportioning limits. The results indicate close proximity between the optimized and code-provided ranges; however, the use of optimal limits can lead to additional cost optimization. Modifications to achieve further optimization are also discussed. Besides the geometric variables, other design parameters not covered by the ACI building code, like reinforcement ratios, bar diameters, and material strengths, and their effects on cost optimization, are also discussed. The findings of this investigation can be used by experienced engineers to refine their designs, without delving into the complexities of optimization.

• Journal of Microbiology and Biotechnology
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• 제25권10호
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• pp.1714-1727
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• 2015

This work describes a new strategy for optimal design of Multiplex-PCR primer sequences. The process is based on the Particle Swarm Optimization-Simplex algorithm (Mult-PSOS). Diverging from previous solutions centered on heuristic tools, the Mult-PSOS is selfconfigured because it does not require the definition of the algorithm's initial search parameters. The successful performance of this method was validated in vitro using Multiplex-PCR assays. For this validation, seven gene sequences of the most prevalent bacteria implicated in urinary tract infections were taken as DNA targets. The in vitro tests confirmed the good performance of the Mult-PSOS, with respect to infectious disease diagnosis, in the rapid and efficient selection of the optimal oligonucleotide sequences for Multiplex-PCRs. The predicted sequences allowed the adequate amplification of all amplicons in a single step (with the correct amount of DNA template and primers), reducing significantly the need for trial and error experiments. In addition, owing to its independence from the initial selection of the heuristic constants, the Mult-PSOS can be employed by non-expert users in computational techniques or in primer design problems.

• Journal of Electrical Engineering and Technology
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• 제11권3호
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• pp.715-723
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• 2016

This paper proposes a design of optimal PI(D) controller for brushless DC (BLDC) motor speed control by the particle swarm optimization (PSO), one of the powerful metaheuristic optimization search techniques. The proposed control system is implemented on the TMS320F28335 DSP board interfacing to MATLAB/SIMULINK. With Back EMF detection, the proposed system is considered as a class of sensorless control. This scheme leads to the speed adjustment of the BLDC motor by PWM. In this work, the BLDC motor of 100 watt is conducted to investigate the control performance. As results, it was found that the speed response of BLDC motor can be regulated at the operating speed of 800 and 1200 rpm in both no load and full load conditions. Very satisfactory responses of the BLDC system can be successfully achieved by the proposed control structure and PSO-based design approach.