• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권4호
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• pp.1106-1127
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• 2012

Service oriented architecture (SOA) lends itself to model the application components to coarse-grained services in such a way that the composition of different services could be feasible. Service composition fulfills numerous service requirements by constructing composite applications with various services. As it is the case in many real-world applications, different users have diverse QoS demands issuing for composite applications. In this paper, we present a service composition framework for a typical service overlay network (SON) considering both multiple QoS constraints and load balancing factors. Moreover, a service selection algorithm based on niching technique and particle swarm optimization (PSO) is proposed for the service composition problem. It supports optimization problems with multiple constraints and objective functions, whether linear or nonlinear. Simulation results show that the proposed algorithm results in an acceptable level of efficiency regarding the service composition objective under different circumstances.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• Structural Engineering and Mechanics
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• 제69권1호
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• pp.21-31
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• 2019

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

• Korean Chemical Engineering Research
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• 제52권4호
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• pp.475-480
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• 2014

플랜트 배치 최적화 문제의 목적은 장치를 연결하는 파이프의 길이를 최소화 하는데 있다. 하지만, 기존 연구들은 대체적으로 단일 층의 배치 문제를 다루고 있으며, 또한 장치 간 유지 보수에 필요한 최소 공간 확보, 사고 예방을 위한 장치 간 이격 거리등 안전 요소를 간과해 왔다. 본 연구에서는 장치 간 유지 보수에 필요한 최소 거리 확보 및 안전 이격 거리를 고려하여 플랜트 배치 문제를 MILP(Mixed Integer Linear Programming) 형태의 문제로 정의하였다. 본 문제의 목적함수는 장치 간 연결하는 파이프 비용이며 제약조건은 안전을 위한 장치 간 최소 이격 거리, 유지 보수에 필요한 공간으로 설정하였다. 하지만, 공정 특성에 따라 필요한 공간 및 작업자의 통행 등 다양한 제약조건을 수반하게 된다. 이에 따라 플랜트 배치 문제를 일반적인 수학식으로 표현하는 데 많은 제약이 있으며, 따라서 함수의 미분식을 이용하는 기존 최적화 방법론을 이용하여 문제를 해결하는 데 많은 어려움이 있다. 본 연구에서는 함수의 미분식을 적용하지 않고 이용이 가능한 경험적 최적화 기법 중 하나인 PSO(Particle Swarm Optimization)를 이용하여 최적화를 수행하였다. 본 연구에서 개발한 모델의 검증을 위해 Ethylene Oxide 공정을 2층으로 배치하는 최적화를 수행하였다.

• Advances in Computational Design
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• 제5권3호
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• pp.209-231
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• 2020

In order to evaluate the performance of three heuristic optimization algorithms, namely, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO) for optimal stacking sequence of laminated composite plates with respect to critical buckling load and non-dimensional natural frequencies, a multi-objective optimization procedure is developed using the weighted summation method. Classical lamination theory and first order shear deformation theory are employed for critical buckling load and natural frequency computations respectively. The analytical critical buckling load and finite element calculation schemes for natural frequencies are validated through the results obtained from literature. The comparative study takes into consideration solution and computational time parameters of the three algorithms in the statistical evaluation scheme. The results indicate that particle swarm optimization (PSO) considerably outperforms the remaining two methods for the special problem considered in the study.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.792-797
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• 2011

본 논문은 배전망에서의 PV (photovoltaic) 발전 시스템의 최적 배치 문제를 이산 입자 군집 최적화 (DPSO, discrete particle swarm optimization)를 이용하여 해를 구할 때 DPSO에 포함되어야 하는 이산화 단계를 위한 하이브리드 이산화 기법의 적용에 대하여 논한다. 이를 위해 PSO 반복단계에서 목적 함수 값과 최적화 속도를 입력 파라미터로 하는 규칙 기반 전문가 시스템을 제안하고 이산 변수를 포함하여 표현되는 PV 시스템 배치 문제의 최적해를 구하는데 적용하였다. 다수준 이산화를 위하여 간단한 라운딩과 sigmoid 함수를 이용한 3단계 및 5단계 이산화 기법을 하이브리드 형태로 적용하였다. 규칙 기반 전문가 시스템을 적용하여 각 PSO 과정에서 적절한 이산화 기법을 선택함으로써 기존의 DPSO보다 좋은 성능의 최적화가 가능하도록 하였다.

• Nuclear Engineering and Technology
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• 제48권4호
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• pp.1022-1035
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• 2016

A double-tube once-through steam generator (DOTSG) consisting of an outer straight tube and an inner helical tube is studied in this work. First, the structure of the DOTSG is optimized by considering two different objective functions. The tube length and the total pressure drop are considered as the first and second objective functions, respectively. Because the DOTSG is divided into the subcooled, boiling, and superheated sections according to the different secondary fluid states, the pitches in the three sections are defined as the optimization variables. A multi-objective optimization model is established and solved by particle swarm optimization. The optimization pitch is small in the subcooled region and superheated region, and large in the boiling region. Considering the availability of the optimum structure at power levels below 100% full power, we propose a new operating scheme that can fix the boundaries between the three heat-transfer sections. The operation scheme is proposed on the basis of data for full power, and the operation parameters are calculated at low power level. The primary inlet and outlet temperatures, as well as flow rate and secondary outlet temperature are changed according to the operation procedure.

• Smart Structures and Systems
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• 제15권5호
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• pp.1253-1270
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• 2015

In this paper Magnetic Charged System Search (MCSS) and Particle Swarm Optimization (PSO) are applied to the problem of damage detection using frequencies and mode shapes of the structures. The objective is to identify the location and extent of multi-damage in structures. Both natural frequencies and mode shapes are used to form the required objective function. To moderate the effect of noise on measured data, a penalty approach is applied. A variety of numerical examples including two beams and two trusses are considered. A comparison between the PSO and MCSS is conducted to show the efficiency of the MCSS in finding the global optimum. The results show that the present methodology can reliably identify damage scenarios using noisy measurements and incomplete data.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.184-189
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• 2014

This paper presents a multi-objective optimization approach to design rotor slot geometry of three-phase squirrel cage induction machine to achieve NEMA design D torque-speed (T-S) characteristics with high efficiency. The multi-objective Particle Swarm Optimization (MOPSO) algorithm combined with the adaptive response surface method and Latin hypercube sampling strategy is applied to obtain the Pareto optimal designs. In order to demonstrate the validity of the suggested optimal algorithm, an application to rotor slot design of three-phase induction motor is presented.

• 전기학회논문지
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• 제67권8호
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• pp.1071-1079
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• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.