Efficient Hybrid Method of FEA‐Based RSM and PSO Algorithm for Multi‐Objective Optimization Design for a Compliant Rotary Joint for Upper Limb Assistive Device

Yang

et al. 2019

Sensors

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.

Section: Related Workmentioning

confidence: 99%

A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization

Yang

et al. 2019

Sensors

Mathematical Problems in Engineering

“…In order to conduct multicriteria, hybrid approaches such as the TM-RSM and GA [35], the TM-fuzzy based on moth-flame optimization [36], PSO-GA [37,38], RSM-PSO [39], gravitational search algorithm (GSA)-GA [40,41], grey-TM, RSM, and entropy measurement [42], PSO-neural network (NN) [43], TM-RSM and NSGA-II [44], and fuzzy logic-ANFIS and lightning attachment procedure optimization (LAPO) [45] have been implemented by many researchers. For more specifics, Dao proposed hybrid approach of the grey-TM according to fuzzy logic for optimizing the main parameters of a compliant stage [46].…”

Section: Introductionmentioning

confidence: 99%

Multiresponse Optimization for a Novel Compliant Z-Stage by a Hybridization of Response Surface Method and Whale Optimization Algorithm

Dang

et al. 2021

Self Cite

A novel compliant z-stage is applied for positioning and indenting a specimen in nano/microindentation testing system. For an excellent operation, the proposed z-stage can concurrently satisfy multicriteria comprising high safety factor, small parasitic motion, and large output displacement. The key aims of this article are to present a novel design of the compliant z-stage as well as an effective integration methodology of Taguchi method, response surface method, weight factor calculation based on signal to noise, and the whale optimization algorithm to resolve a design optimal problem so as to enrich the quality performances of the proposed stage. Primarily, the z-stage is designed based on four-lever amplifier, compliant hinge shifted arrangement mechanism, zigzag-based flexure spring guiding mechanism, and symmetric six leaf hinges-based parallel guiding mechanism. Secondly, the number experiment data are achieved by the Taguchi method and finite element analysis. Subsequently, the regression functions among input variables and quality characteristics are formed by exploiting response surface method. In addition, the weight factors for every characteristic are defined. Additionally, the sensitivity analysis is accomplished for determining influences of input variables on quality responses. Ultimately, based on regression equations, the whale optimization algorithm is executed to define the optimal factors. The consequences indicated that the output deformation is about 454.55 μm and the safety factor is around 2.38. Furthermore, the errors among the optimal consequences and the confirmations for the safety factor and output deformation are 7.12% and 4.25%, correspondingly. By using Wilcoxon and Friedman methods, the results revealed that the proposed algorithm is better than the cuckoo search algorithm. Based on the quality convergence characteristics of hybrid approach, the proposed method is proficient for resolving complicated multiobjective optimization.

Mathematical Problems in Engineering

“…For instance, multiobjective evolutionary optimization is for image encryption [20], for fabrication biocompatible [21], for human manner recognition [22], for microarray cancer data categorization [23], for visibility enhancement and mass division of mammogram figures [24], for hydraulic machinery in engine velocity decrease [25], and for multi-image fusion [26]. Furthermore, there have been combination of different techniques; e.g., the TM was combined with response surface method (RSM) and genetic algorithm (GA) [27], GA-artificial neural network (ANN) and PSO-ANN [28], the TM-fuzzy according to moth-flame optimization [29], particle swarm optimization (PSO) [30] and GA [31], RSM-PSO [32] and gravitational search algorithm-GA [33,34], Grey, TM, RSM and entropy measurement [35], fuzzy logic-ANFIS-based lightning attachment procedure optimization [36], TM-RSM, improved ANFIS, and TLBO [37]. For more specifics, Shrivastava and Singh [38] developed a combined method of RSM and multiobjective genetic algorithm for evaluation of enduring cutting region in CNC turning.…”

Section: Introductionmentioning

confidence: 99%

Optimization of CNC Milling Parameters for Complex 3D Surfaces of SIMOLD 2083 Alloy Mold Core Utilizing Multiobjective Water Cycle Algorithm

Tran

Dang

et al. 2021

Self Cite

In this article, an effective multiobjective optimization approach is exploited to search for the best milling parameters for CNC for complex 3d surfaces of SIMOLD 2083 alloy mold core. To improve the quality responses, the cutting factors are optimized by a combination of Taguchi method (TM), response surface method (RSM), and multiobjective water cycle algorithm (MWCA). Firstly, the design for initial series experiments of the cutting factors was generated via the TM. Thereafter, the regression models between the cutting factors and the surface roughness of the machined workpiece surface as well as milling time are formed via applying the RSM. Moreover, analysis of variance and sensitivity analysis are also executed to define the influences and crucial contributions of cutting parameters on the surface roughness and milling time. The results of analysis of variance showed that the factors which have main effects on surface roughness were spindle speed (42.42%), feed rate (29.40%), and cutting depth (6.59%), respectively. Meanwhile, the feed rate with the influence of 92.6% was the most significant factor in controlling the milling time. Ultimately, based on mathematical models, the MWCA is performed to define the optimal factors. The optimal results indicated that the optimized surface roughness was about 0.260 μm and the milling time was roughly 1012.767 (s). In addition, the errors between forecasted results and experimental verifications for the surface roughness and milling time are 2.04% and 5.39%, respectively. Therefore, the results of experimental verifications are suitable with the forecasted results from the proposed optimization method. These results depicted that the proposed integration approach can define effectively the optimal cutting factors for CNC milling and expand to apply for complex multiobjective optimization problems.