One Improved Method for Analytic Hierarchy Process Based on Least Deviation Method and Particle Swarm Optimization

2007 ◽  
Author(s):  
Jianguo Zhou ◽  
Weijing Yang ◽  
Yu Shi ◽  
Zhaoming Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Analytic Hierarchy Process ◽  
Particle Swarm ◽  
Improved Method ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Hierarchy Process ◽  
Deviation Method

scholarly journals Research on sensory comfort of tight-fitting sportswear based on intelligent models

2021 ◽  
Vol 16 ◽  
pp. 155892502110680
Author(s):  
Pengpeng Cheng ◽  
Jianping Wang ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Xuyuan Tao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Analytic Hierarchy Process ◽  
Rough Set Theory ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Entropy Weight ◽  
Fuzzy Rough Set ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Hierarchy Process

In order to study the influence of human body parts on the overall comfort under different sports conditions, this paper designed a series of actions such as standing, squatting, running, walking, and so on, and obtained the key parts that affected the overall comfort at every experimental stage (i.e. every motion state) through subjective evaluation. That is, to study and analyze the comfort evaluation of every part and the whole body under different motions conditions, as well as the main parts that affect the overall comfort. In this paper, Analytic Hierarchy Process-Entropy weight, Fuzzy-Rough Set Theory, Analytic Hierarchy Process-Structural Equation Model, and Particle Swarm Optimization-Cuckoo Search were used to optimize the position index. At last, the prediction model of overall comfort was established by Adaptive Network-based Fuzzy Influence System. The input parameters are body part indexes screened by Analytic Hierarchy Process-Entropy weight, Fuzzy-Rough Set Theory, Analytic Hierarchy Process-Structural Equation Model and Particle Swarm Optimization-Cuckoo Search, respectively. And the output is the overall comfort evaluation value. Compared with the real value of overall comfort in every experimental stage, the effectiveness of Analytic Hierarchy Process-Entropy weight, Fuzzy-Rough Set Theory, Analytic Hierarchy Process-Structural Equation Model, and Particle Swarm Optimization-Cuckoo Search optimizing indexes is verified. The results show that: (1) About index optimization models, Particle Swarm Optimization-Cuckoo Search and Analytic Hierarchy Process-Entropy weight are better than Fuzzy-Rough Set Theory, so both Particle Swarm Optimization-Cuckoo Search and Analytic Hierarchy Process-Entropy weight could optimize index predicting overall comfort. (2) Different movements have great differences in the parts that affect the overall comfort.

scholarly journals Multi-Objective Optimization Design of an Electrohydrostatic Actuator Based on a Particle Swarm Optimization Algorithm and an Analytic Hierarchy Process

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2426 ◽  
Author(s):  
Bo Yu ◽  
Shuai Wu ◽  
Zongxia Jiao ◽  
Yaoxing Shang
Keyword(s):  
Particle Swarm Optimization ◽  
Power Consumption ◽  
Analytic Hierarchy Process ◽  
Optimization Design ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Hierarchy Process

During the last few years, the concept of more-electric aircraft has been pushed ahead by industry and academics. For a more-electric actuation system, the electrohydrostatic actuator (EHA) has shown its potential for better reliability, low maintenance cost and reducing aircraft weight. Designing an EHA for aviation applications is a hard task, which should balance several inconsistent objectives simultaneously, such as weight, stiffness and power consumption. This work presents a method to obtain the optimal EHA, which combines multi-objective optimization with a synthetic decision method, that is, a multi-objective optimization design method, that can combine designers’ preferences and experiences. The evaluation model of an EHA in terms of weight, stiffness and power consumption is studied in the first section. Then, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced to obtain the Pareto front, and an analytic hierarchy process (AHP) is applied to help find the optimal design in the Pareto front. A demo of an EHA design illustrates the feasibility of the proposed method.

Study on Warship Combat System Design Using DMOPSO Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 1963-1968 ◽  
Author(s):  
Wei Ren ◽  
Ying Xiong ◽  
Hong Mei Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Topsis Method ◽  
Analytic Hierarchy ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Design Variables ◽  
Measure Of Performance ◽  
Hierarchy Process

A new method for selecting warship combat system during the period of warship alternatives conceptual design was discussed in the paper. After computing the overall measure of performance (OMOE) and overall measure of risk (OMOR) of all combat sub-system using analytic hierarchy process and utility function, the design variables representing this equipment alternatives were selected using discrete particle swarm. Niche technique was used for constructing non-dominated sort set and TOPSIS method was adopted to sort the final pareto solution. The results of selecting equipment alternatives in warship combat system show that DMOPSO (discrete multi-objective particle swarm optimization) can search the overall pareto solution effectively.

Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process

2014 ◽  
Vol 28 (1) ◽  
pp. 71-82 ◽  
Author(s):  
Yan-Juan Hu ◽  
Yao Wang ◽  
Zhan-Li Wang ◽  
Yi-Qiang Wang ◽  
Bang-Cheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Analytic Hierarchy Process ◽  
Decision Maker ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Scheme Selection ◽  
Hierarchy Process ◽  
Machining Scheme

AbstractThe goal of machining scheme selection (MSS) is to select the most appropriate machining scheme for a previously designed part, for which the decision maker must take several aspects into consideration. Because many of these aspects may be conflicting, such as time, cost, quality, profit, resource utilization, and so on, the problem is rendered as a multiobjective one. Consequently, we consider a multiobjective optimization problem of MSS in this study, where production profit and machining quality are to be maximized while production cost and production time must be minimized, simultaneously. This paper presents a new discrete method for particle swarm optimization, which can be widely applied in MSS to find out the set of Pareto-optimal solutions for multiobjective optimization. To deal with multiple objectives and enable the decision maker to make decisions according to different demands on each evaluation index, an analytic hierarchy process is implemented to determine the weight value of evaluation indices. Case study is included to demonstrate the feasibility and robustness of the hybrid algorithm. It is shown from the case study that the multiobjective optimization model can simply, effectively, and objectively select the optimal machining scheme according to the different demands on evaluation indices.

scholarly journals An Improved Method for NURBS Surface Based on Particle Swarm Optimization BP Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Nurbs Surface ◽  
Improved Method ◽  
Swarm Optimization
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