scholarly journals Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qingquan Jiang ◽  
Xiaoya Liao ◽  
Rui Zhang ◽  
Qiaozhen Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Production Scheduling ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Total Weighted Tardiness ◽  
Production Environment ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

A single-machine scheduling problem that minimizes the total weighted tardiness with energy consumption constraints in the actual production environment is studied in this paper. Based on the properties of the problem, an improved particle swarm optimization (PSO) algorithm embedded with a local search strategy (PSO-LS) is designed to solve this problem. To evaluate the algorithm, some computational experiments are carried out using PSO-LS, basic PSO, and a genetic algorithm (GA). Before the comparison experiment, the Taguchi method is used to select appropriate parameter values for these three algorithms since heuristic algorithms rely heavily on their parameters. The experimental results show that the improved PSO-LS algorithm has considerable advantages over the basic PSO and GA, especially for large-scale problems.

scholarly journals An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Kun Li ◽  
Huixin Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Particle Swarm ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Setup Costs ◽  
Sequence Dependent Setup ◽  
Sequence Dependent Setup Costs

This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO) is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

scholarly journals Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Houxian Zhang ◽  
Zhaolan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Large Scale Network ◽  
Scale Network

No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

scholarly journals Enhancing Power Quality in PV-SOFC Microgrids Using Improved Particle Swarm Optimization

2019 ◽  
Vol 9 (5) ◽  
pp. 4616-4622
Author(s):  
V. V. Prabhakaran ◽  
A. Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Power Quality ◽  
Electricity Markets ◽  
Large Scale ◽  
Control Strategies ◽  
Particle Swarm ◽  
Voltage Source ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The concept of hybrid microgrid (MG) has attracted tremendous attention in modern electricity markets, owing to the enhanced efficiency and reliability it offers to the main electricity grid. Numerous meritorious aspects associated with hybrid MGs are the key features of future large scale renewable technologies. In this paper, a hybrid MG using PV-SOFC (PhotoVoltaic – Solid Oxide Fuel Cell) is connected to an infinite bus bar, in order to achieve an autonomous working mode. The dynamic and steady-state operation with control strategies for both PV and SOFC power systems are analyzed. The objective is to control the voltage and frequency of the MG when it is not connected to the main grid. Typically, an efficient control strategy must assess the power conversion system and its state, in the isolated MG. Moreover, it must reliably handle variant and intermittent type of loads. With this viewpoint, we propose a Voltage Source Inverter (VSI) based Proportional Integral (PI) controller, optimized by Improved Particle Swarm Optimization (IPSO) for the purpose of smooth power flow control improving power quality. The performance of PI-IPSO and PI technologies are evaluated, for the proposed MG, in MATLAB/Simulink. The results obtained verify the effectiveness of the modified PSO algorithm, in comparison to the conventional PI techniques, for the frequency and voltage control of the MG.

An Improved PSO-ACO Algorithm for Solving Large-Scale TSP

2010 ◽  
Vol 143-144 ◽  
pp. 1154-1158 ◽  
Author(s):  
Ai Jia Ouyang ◽  
Yong Quan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Shortest Paths ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Improved Pso

In this paper, an improved particle swarm optimization-ant colony algorithm (PSO-ACO) is presented by inserting delete-crossover strategy into it for the shortcoming which PSO-ACO can’t solve the large-scale TSP. The experiments results show that the PSO-ACO has better performance than ant colony algorithm (ACO) on searching the shortest paths, error and robustness for the TSP.

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