Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

2017 ◽  
Vol 31 (15) ◽  
pp. 4835-4853 ◽  
Author(s):  
Mojtaba Shourian ◽  
S. Jamshid Mousavi
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Assessment ◽  
Network Flow ◽  
Water Allocation ◽  
Programming Model ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Flow Programming ◽  
Optimum Water

scholarly journals The application of particle swarm optimization using neural network to optimize classification of employee performance assessment

2019 ◽  
Vol 1175 ◽  
pp. 012067
Author(s):  
Mohammad Humam ◽  
Oman Somantri ◽  
Maylane Boni Abdillah ◽  
Syaefani Arif Romadhon ◽  
Mohammad Khambali ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Performance Assessment ◽  
Employee Performance ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yi-Ling Wu ◽  
Tsu-Feng Ho ◽  
Shyong Jian Shyu ◽  
Bertrand M. T. Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Model ◽  
Particle Swarm ◽  
Solution Space ◽  
Discrete Particle Swarm Optimization ◽  
Binary Matrix ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Candidate Solution ◽  
Departmental Budget

Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DPSO) with scout particles, where each particle, represented as a binary matrix, corresponds to a candidate solution to the problem. An initialization algorithm and a penalty function are designed to cope with the constraints, and the scout particles are employed to enhance the exploration within the solution space. To demonstrate the effectiveness and efficiency of the proposed DPSO, a series of computational experiments are designed and conducted. The results are statistically analyzed, and it is evinced that the proposed DPSO is an effective approach for the studied problem.

scholarly journals Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jiaxi Wang ◽  
Boliang Lin ◽  
Junchen Jin
Keyword(s):  
Particle Swarm Optimization ◽  
High Speed ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Programming Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Enhanced Particle Swarm Optimization ◽  
Multiple Units

The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.

Research on Engineering Project Time-Cost Optimization Based on Particle Swarm Optimization

2011 ◽  
Vol 55-57 ◽  
pp. 1683-1686
Author(s):  
Ting Wang ◽  
Li Feng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Model ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Time Cost ◽  
Engineering Project ◽  
Swarm Optimization ◽  
Nonlinear Relation

In order to reasonably reduce the cost of project, and reduce the duration of project, the engineering project time–cost must be optimized. The paper concludes the project time - cost optimal solution, by establishing programming model of project time–cost nonlinear relation, and using particle swarm optimization algorithm to achieve progress optimization. And using an example shows that this optimization method is the feasibility and practicability in solving engineering project time–cost of nonlinear optimization.

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