scholarly journals An Efficient Particle Swarm Optimizer with Application to Man-Day Project Scheduling Problems

2014 ◽  
Author(s):  
Frode Eika Sandnes ◽  
Ruey Maw Chen
Keyword(s):  
Project Scheduling ◽  
Particle Swarm ◽  
Scheduling Problems ◽  
Particle Swarm Optimizer

scholarly journals An Efficient Particle Swarm Optimizer with Application to Man-Day Project Scheduling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Frode Eika Sandnes
Keyword(s):  
Project Scheduling ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Np Hard ◽  
Scheduling Problems ◽  
Particle Swarm Optimizer ◽  
Np Hard Problem ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Mode Assignment

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfully.

scholarly journals Probability Mechanism Based Particle Swarm Optimization Algorithm and Its Application in Resource-Constrained Project Scheduling Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Shuai Li ◽  
Zhicong Zhang ◽  
Xiaohui Yan ◽  
Liangwei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Project Scheduling ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Resource Constrained Project Scheduling ◽  
Combinatorial Optimization Problems ◽  
New Particles

In this paper, a new probability mechanism based particle swarm optimization (PMPSO) algorithm is proposed to solve combinatorial optimization problems. Based on the idea of traditional PSO, the algorithm generates new particles based on the optimal particles in the population and the historical optimal particles in the individual changes. In our algorithm, new particles are generated by a specially designed probability selection mechanism. We adjust the probability of each child element in the new particle generation based on the difference between the best particles and the elements of each particle. To this end, we redefine the speed, position, and arithmetic symbols in the PMPSO algorithm. To test the performance of PMPSO, we used PMPSO to solve resource-constrained project scheduling problems. Experimental results validated the efficacy of the algorithm.

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Classification System ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Classification Performance ◽  
Decision Boundary ◽  
Particle Swarm Optimizer ◽  
Selection Phase

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

scholarly journals Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

2021 ◽  
Vol 11 (3) ◽  
pp. 1325
Author(s):  
Dalia Yousri ◽  
Magdy B. Eteiba ◽  
Ahmed F. Zobaa ◽  
Dalia Allam
Keyword(s):  
Permanent Magnet ◽  
Linear Optimization ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Synchronous Motor ◽  
Variable Order ◽  
Particle Swarm Optimizer ◽  
Novel Variants ◽  
Fractional Models ◽  
Complex Nonlinear Systems

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.

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