Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm

In order to fit in with the demands of the development of electricity market in China, a multi-objective optimization mathematical model is presented to dispatch load within the units, taking economy, environmental protection and quick responsiveness to dispatching commands into consideration at the same time. And take the minimal whole plants power-supply coal cost rate, the minimal pollutant emissions and the minimal load adjusting time as these three objective functions respectively. The four constraint conditions are unit power balance constraint, load bound constraint, ramping constraint and pollution discharge standards constraint. An improved particle swarm optimization algorithm is used to get the Pareto solution set. The optimal solution was obtained by using the method of multi-attribute decision making, through sequencing the solution set by comprehensive evaluation. A case study based on a power plant with 4×600MW units was carried out. The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.

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Multi Objective Optimization of Cutting Process Based on Improved Particle Swarm Optimization Algorithm

Materials Science Forum ◽

10.4028/www.scientific.net/msf.800-801.688 ◽

2014 ◽

Vol 800-801 ◽

pp. 688-692

Author(s):

Xue Liu ◽

Cai Xu Yue ◽

Ming Yang Wu ◽

Yong Heng Yang ◽

Guang Xu Ren ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Cutting Force ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Cutting Temperature ◽

Particle Swarm ◽

Multi Objective Optimization ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Multi Objective

With the rapid development of machinery industry, the processing parameters which affect on the quality of the products in machining and measuring index also appeared diversified, which makes the study of multi-objective optimization problem is very important. Among the many factors, cutting consumption plays a key role in many indicators, in effect, cutting force and cutting temperature on the quality and performance of products is the most prominent, so this paper takes PCBN tool cutting Cr12MoV steel as the experimental basis, the cutting parameters to optimize the parameters;the cutting force and cutting temperature as the index;with the aid of the BP neural network modeling of cutting force and cutting temperature, at the same time, this paper improved particle swarm optimization algorithm to achieve multiple objectives, provides multi objective optimization parameters more reliable for die steel production process.

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An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686718500221 ◽

2018 ◽

Vol 17 (03) ◽

pp. 375-390 ◽

Cited By ~ 8

Author(s):

Fuqiang Zhang ◽

Jingjing Li

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Manufacturing Systems ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Automated Guided Vehicles ◽

Raw Material ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Scheduling Model

To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.

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