THE SCALING OF HYBRID METHOD IN SOLVING UNCONSTRAINED OPTIMIZATION METHOD

2016 ◽  
Vol 99 (7) ◽  
pp. 983-991 ◽  
Author(s):  
Mohd Asrul Hery Ibrahim ◽  
Mustafa Mamat ◽  
Puspa Liza Ghazali ◽  
Zabidin Salleh
Keyword(s):  
Unconstrained Optimization ◽  
Hybrid Method ◽  
Optimization Method

scholarly journals A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2033
Author(s):  
Raegeun Oh ◽  
Yifang Shi ◽  
Jee Woong Choi
Keyword(s):  
Particle Swarm Optimization ◽  
Motion Analysis ◽  
Hybrid Method ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Optimization Method ◽  
Batch Processing ◽  
Swarm Optimization ◽  
Newton Raphson ◽  
Target Motion Analysis

Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton–Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton–Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.

A sequential hybrid method to establish practical sizing systems based on anthropometric data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Tjolleng ◽  
Kihyo Jung ◽  
Hyunsook Han ◽  
Hyunjung Han ◽  
Jayoung Cho
Keyword(s):  
Hybrid Method ◽  
Grid Method ◽  
Initial Step ◽  
Optimization Methods ◽  
Optimization Method ◽  
Anthropometric Data ◽  
Content Type ◽  
Subsequent Step ◽  
Sizing System ◽  
Sizing Systems

PurposeSize fit and economic efficiency are two crucial aspects that need to be considered in designing a sizing system. However, there could exist a trade-off between those aspects in order to establish a practical sizing system. The purpose of this paper is to develop a sequential hybrid method of grid and optimization to generate a practical sizing system using anthropometric data.Design/methodology/approachThe proposed sequential hybrid method consisted of two sequential steps, which employs grid method and optimization method. In the initial step, the grid method creates primary grids that accommodate a designated percentage (e.g. 90%) of users with best size fit. In the subsequent step, the optimization method generated additional grids to provide acceptable fit, with minimum fit penalty scores for users unaccommodated by the primary grids. Our method was applied to the development of a sizing system for men's military jackets. The proposed method performances were evaluated in terms of accommodation percentage, size fit and number of sizing categories.FindingsOur proposed method resulted in 26 primary grids during the initial step, which cover 90% of users. Next, we generated six additional grids during the subsequent step that provide minimum fit penalty scores for the rest (10%) users.Originality/valueThe main contributions of this paper are as follows: consider accommodation percentage, size fit and number of sizing categories in the design of sizing system; combine the grid and optimization methods and evaluate a sizing system for men's military jackets. The proposed method is applicable to develop optimal sizing systems for multiple-size products.

scholarly journals The Solution of Two-Phase Inverse Stefan Problem Based on a Hybrid Method with Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yang Yu ◽  
Xiaochuan Luo ◽  
Haijuan Cui
Keyword(s):  
Conjugate Gradient Method ◽  
Stefan Problem ◽  
Hybrid Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Simulation Experiment ◽  
Adomian Decomposition Method ◽  
Optimization Method ◽  
Two Phase ◽  
Adomian Decomposition

The two-phase Stefan problem is widely used in industrial field. This paper focuses on solving the two-phase inverse Stefan problem when the interface moving is unknown, which is more realistic from the practical point of view. With the help of optimization method, the paper presents a hybrid method which combines the homotopy perturbation method with the improved Adomian decomposition method to solve this problem. Simulation experiment demonstrates the validity of this method. Optimization method plays a very important role in this paper, so we propose a modified spectral DY conjugate gradient method. And the convergence of this method is given. Simulation experiment illustrates the effectiveness of this modified spectral DY conjugate gradient method.

scholarly journals Pareto Assumption for Constrained PSO-Based Locomotive Resistance Minimization

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Mine Sertsöz ◽  
Mehmet Fidan
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Method ◽  
Particle Swarm ◽  
Optimization Method ◽  
Optimal Operation ◽  
Mechanical Resistance ◽  
Swarm Optimization ◽  
Pareto Analysis ◽  
Operation Conditions ◽  
Particle Swarm Method

The mechanical resistance of a locomotive is crucial for power consumption. It is desirable to maintain this resistance at a minimum value for energy efficiency under optimal operation conditions. The optimal conditions can be found by particle swarm optimization with constraints. The particle swarm optimization method is a highly preferred type of heuristic algorithm because of its advantages, such as fewer parameters, faster speed, and a simpler flow diagram. However, fast convergence can be misleading in finding the optimum solution in some cases. Pareto analysis is used in this proposed study to prevent missing the target. When the literature is searched, it is seen that there are various studies using this method. However, in all of these studies, the results of the particle swarm method have been interpreted as whether or not they complied with Pareto’s 80/20 rule. The validity of the Pareto analysis is taken as an assumption, and with the help of this assumption, the coefficients of a locomotive’s mathematical equation were changed, and finally the results were found by applying the particle herd optimization method. Finally, a novel hybrid method has been created by including the Pareto optimality condition to particle swarm optimization. The results are compared with this innovative hybrid method of Pareto and particle swarm and the results found using only the particle swarm method.

scholarly journals Study of a New Hybrid Optimization-Based Method for Obtaining Parameter Values of Solar Cells

2020 ◽  
Author(s):  
Selma Tchoketch_Kebir
Keyword(s):  
Solar Cells ◽  
Hybrid Method ◽  
Traditional Method ◽  
Hybrid Methods ◽  
Optimization Method ◽  
Parameter Determination ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unknown Parameters ◽  
Parameter Values

This chapter presents a comprehensive study of a new hybrid method developed for obtaining the electrical unknown parameters of solar cells. The combination of a traditional method and a recent smart swarm-based optimization method is done, with a big focus on the application of the topic of artificial intelligence algorithms into solar photovoltaic production. The combined approach was done between the traditional method, which is the noniterative Levenberg-Marquardt technic and between the recent meta-heuristic optimization technic, called Grey Wolf optimizer algorithm. For comparison purposes, some other classical solar cell parameter determination optimization-based methods are carried out, such as the numerical (iterative, noniterative) methods, the meta-heuristics (evolution, human, physic, and swarm) methods, and other hybrid methods. The final obtained results show that the used hybrid method outperforms the above-mentioned classical methods, under this study.

scholarly journals A New Unconstrained Optimization Method for Imprecise Function and Gradient Values

1996 ◽  
Vol 197 (2) ◽  
pp. 586-607 ◽  
Author(s):  
M.N. Vrahatis ◽  
G.S. Androulakis ◽  
G.E. Manoussakis
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Method
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