Reliability Optimization: A Particle Swarm Approach

2016 ◽  
pp. 163-187 ◽  
Author(s):  
Sangeeta Pant ◽  
Anuj Kumar ◽  
Mangey Ram
Keyword(s):  
Particle Swarm ◽  
Reliability Optimization

scholarly journals An adaptive particle swarm optimization method for multi-objective system reliability optimization

2019 ◽  
Vol 233 (6) ◽  
pp. 990-1001 ◽  
Author(s):  
Mohamed Arezki Mellal ◽  
Enrico Zio
Keyword(s):  
Particle Swarm Optimization ◽  
System Reliability ◽  
Particle Swarm ◽  
Superior Performance ◽  
Reliability Optimization ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Multi Objective ◽  
Objective System ◽  
System Reliability Optimization

Multi-objective system reliability optimization has attracted the attention of several researchers, due to its importance in industry. In practice, the optimization regards multiple objectives, for example, maximize the reliability, minimize the cost, weight, and volume. In this article, an adaptive particle swarm optimization is presented for multi-objective system reliability optimization. The approach uses a Lévy flight for some particles of the swarm, for avoiding local optima and insuring diversity in the exploration of the search space. The multi-objective problem is converted to a single-objective problem by resorting to the weighted-sum method and a penalty function is implemented to handle the constraints. Nine numerical case studies are presented as benchmark problems for comparison; the results show that the proposed approach has superior performance than a standard particle swarm optimization.

A Particle Swarm Optimization-Based Approach for Finding Reliability in a Total Hip Prosthesis

2021 ◽  
pp. 222-242
Author(s):  
Bouakkar Loubna ◽  
Ameddah Hacene ◽  
Mazouz Hammoud
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Optimization Problems ◽  
Hip Prosthesis ◽  
Particle Swarm ◽  
Reliability Optimization ◽  
Design Phase ◽  
Total Hip Prosthesis ◽  
Swarm Optimization ◽  
Total Hip

Nowadays, we assist the global extension of reliability optimization problems from the design phase of systems and sub-systems to the design and operational phases, not only of systems and sub-systems, but also of bio functionality design. This chapter investigates the relative performances of particle swarm optimization (PSO) variants when used to find reliability in the total hip prosthesis by finding the maximization of jumping distance (JD) to avoid dislocation and the minimization of system's stability to offer mobility. Statistical analysis of different cases of head diameters of 22, 28, 36, 40 mm has been conducted to survey the convergence and relative performances of the main PSO variants when applied to solve reliability in the total hip prosthesis.

Interactive enhanced particle swarm optimization: A multi-objective reliability application

2007 ◽  
Vol 221 (3) ◽  
pp. 177-191 ◽  
Author(s):  
M. K. Pandey ◽  
M. K. Tiwari ◽  
M. J. Zuo
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Reliability Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New Variant ◽  
Conflicting Objectives ◽  
Enhanced Particle Swarm Optimization

In reliability optimization problems, it is desirable to address different conflicting objectives. This generally includes maximization of system reliability and minimization of cost, weight, and volume. The proposed algorithm of a metaheuristic nature is designed to address multi-objective problems. In the presented algorithm, interaction with a decision maker guides the search towards the preferred solution. A comparison between an existing solution and the newly generated solution substantiates the desirability or fitness of the latter. Further, the utility function expresses the preference information of the decision maker while searching for the best solution. During the development of the algorithm, a new variant of particle swarm optimization (PSO) is proposed and named as ‘enhanced particle swarm optimization’ (EPSO). EPSO considers the difference between the particle's best position and the global best position for efficient search and convergence. The developed algorithm is applied to the reliability optimization problem of a multistage mixed system with four different value functions that are used to simulate the designer's opinion in the solution evaluation process. Results indicate that the algorithm effectively captures the decision maker's preferences for different structures. Superior results in multi-objective reliability problem-solving prove the algorithm's superiority over other approaches.

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