• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.490-498
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• 2016

Proportional Integral Derivative (PID) controller tuning is an area of interest for researchers in many areas of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of bacteria foraging and particle swarm optimization. BFO algorithm has recently emerged as a very powerful technique for real parameter optimization. To overcome delay in an optimization, combine the features of BFOA and PSO for tuning the PID controller. This new algorithm is proposed to combine both the algorithms to get better optimization values. The real time prototype model of paper machine is designed and controlled by using PIC microcontroller embedded with the programming in C language.

• Computers and Concrete
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• v.19 no.2
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• pp.127-131
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• 2017

This paper presents a generalized formulation for optimizing the design of concrete beam reinforced with glass fiber reinforced polymer bar. The optimization method is formulated to find the design variables leading to the minimum weight of concrete beam with constraints imposed based on ACI code provisions. A simple genetic algorithm is utilized to solve the optimization task. The weights of concrete and glass fiber reinforced polymer bar are included in the formulation of the objective function. The ultimate limit states and the serviceability limit states are included in formulation of constraints. The results of illustrated example demonstrate the efficiency of the proposed method to reduce the weight of beam as well as to satisfy the above requirement. The application of the optimization based on the most economical design concept have led to significant savings in the amount of the component materials to be used in comparison to classical design solutions.

• International Journal of CAD/CAM
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• v.7 no.1
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• pp.11-20
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• 2007

A topology optimization methodology, named "smooth boundary topology optimization," is proposed to overcome the shortcomings of cell-based methods. Material boundary is represented by B-spline curves and their control points are considered as design variables. The design is improved by either creating a hole or moving control points. To determine which is more beneficial, a selection criterion is defined. Once determined to create a hole, it is represented by a new B-spline and recognized as a new boundary. Because the proposed method deals with the control points of B-spline as design variables, their total number is much smaller than cell-based methods and it ensures smooth boundaries. Differences between our method and level set method are also discussed. It is shown that our method is a natural way of obtaining smooth boundary topology design effectively combining computer graphics technique and design sensitivity analysis.

• Smart Structures and Systems
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• v.21 no.3
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• pp.321-333
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• 2018

Simplified Dolphin Echolocation (SDE) algorithm is a recently developed meta-heuristic algorithm. This algorithm is an improved and simplified version of the Dolphin Echolocation Optimization (DEO) method, based on the baiting behavior of the dolphins. The main advantage of the SDE algorithm is that it needs no empirical parameter. In this paper, the SDE algorithm is applied for optimization of three well-studied frame structures. The designs are then compared with those of other meta-heuristic methods from the literature. Numerical results show the efficiency of the SDE algorithm and its competitive ability with other well-established meta-heuristics methods.

• Structural Engineering and Mechanics
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• v.70 no.5
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• Advances in Energy Research
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• v.7 no.1
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• pp.35-52
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• 2020

The paper proposes a hybrid approach of artificial bee colony (ABC) and grey wolf optimizer (GWO) algorithm for multi-objective and multidimensional engine optimization of a converted plug-in hybrid electric vehicle. The proposed strategy is used to optimize all emissions along with brake specific fuel consumption (FC) for converted parallel operated diesel plug-in hybrid electric vehicle (PHEV). All emissions particulate matter (PM), nitrogen oxide (NOx), carbon monoxide (CO) and hydrocarbon (HC) are considered as optimization parameters with weighted factors. 70 hp engine data of NOx, PM, HC, CO and FC obtained from Oak Ridge National Laboratory is used for the study. The algorithm is initialized with feasible solutions followed by the employee bee phase of artificial bee colony algorithm to provide exploitation. Onlooker and scout bee phase is replaced by GWO algorithm to provide exploration. MATLAB program is used for simulation. Hybrid ABC-GWO algorithm developed is tested extensively for various values of speeds and torque. The optimization performance and its environmental impact are discussed in detail. The optimization results obtained are verified by real data engine maps. It is also compared with modified ABC and GWO algorithm for checking the effectiveness of proposed algorithm. Hybrid ABC-GWO offers combine benefits of ABC and GWO by reducing computational load and complexity with less computation time providing a balance of exploitation and exploration and passes repeatability towards use for real-time optimization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.1979-1995
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• 2015

Network convergence is regarded as the development tendency of the future wireless networks, for which self-organization paradigms provide a promising solution to alleviate the upgrading capital expenditures (CAPEX) and operating expenditures (OPEX). Self-optimization, as a critical functionality of self-organization, employs a decentralized paradigm to dynamically adapt the varying environmental circumstances while without relying on centralized control or human intervention. In this paper, we present comprehensive surveys of heterogeneous networks (HetNets) and investigate the enhanced self-optimization models. Self-optimization approaches such as dynamic mobile access network selection, spectrum resource allocation and power control for HetNets, etc., are surveyed and compared, with possible methodologies to achieve self-optimization summarized. We hope this survey paper can provide the insight and the roadmap for future research efforts in the self-optimization of convergence networks.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.1
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• pp.119-125
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• 2005

The current automotive is seeking the improvement of performance, the prevention of environmental pollution and the saving of energy resources according to miniaturization and lightweight of the components. And the variance analysis on the basis of structure analysis and DOE is applied to the lower control am. We have proposed a statistical design model to evaluate the effect of structural modification by performing the practical multi-objective optimization considering weight, stress and fatigue lift. The lower control arm is performed the fatigue analysis using the load history of real road test. The design model is determined using the optimization of acquired load history with the fatigue characteristic. The characteristic function is made use of the optimization according to fatigue characteristics to consider constrained function in the optimization of DOE. The structure optimization of a lower control arm according to fatigue characteristics is performed. And the optimized design variable is D=47 m, T=36mm, W=12 mm. In the real engineering problem of considering many objective functions, the multi-objective optimization process using the mathematical programming and the characteristic function is derived an useful design solution.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Structural Engineering and Mechanics
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• v.59 no.5
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• pp.933-950
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• 2016

Decks, interior beams, edge beams and girders are the parts of a steel floor system. If the deck is optimized without considering beam optimization, finding best result is simple. However, a deck with higher cost may increase the composite action of the beams and decrease the beam cost reducing the total cost. Also different number of floor divisions can improve the total floor cost. Increasing beam capacity by using castellated beams is other efficient method to save the costs. In this study, floor optimization is performed and these three issues are discussed. Floor division number and deck sections are some of the variables. Also for each beam, profile section of the beam, beam cutting depth, cutting angle, spacing between holes and number of filled holes at the ends of castellated beams are other variables. Constraints include the application of stress, stability, deflection and vibration limitations according to the load and resistance factor (LRFD) design. Objective function is the total cost of the floor consisting of the steel profile cost, cutting and welding cost, concrete cost, steel deck cost, shear stud cost and construction costs. Optimization is performed by enhanced colliding body optimization (ECBO), Results show that using castellated beams, selecting a deck with higher price and considering different number of floor divisions can decrease the total cost of the floor.