Reliability Optimization Allocation Method Based on Improved Dynamic Particle Swarm Optimization

2019 ◽  
Author(s):  
Qihai Liang ◽  
Zhe Wang ◽  
Jinzhu Qu ◽  
Xiaojian Yi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Reliability Optimization ◽  
Swarm Optimization ◽  
Allocation Method ◽  
Optimization Allocation

scholarly journals An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm

2018 ◽  
Vol 36 (4) ◽  
pp. 722-727
Author(s):  
Guoqing Shi ◽  
Fan Wu ◽  
Lin Zhang ◽  
Shuyang Zhang ◽  
Cao Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Allocation Method

The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.

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.

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