scholarly journals Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4114
Author(s):  
Yang Liu ◽  
Zihang Zhang ◽  
Lei Bo ◽  
Dongxu Zhu

This paper proposes a general hierarchical dispatching strategy of mine water, with the aim of addressing the problems of low reuse rate of coal mine water, and insufficient data analysis. First of all, water quality and quantity data of the Narim River No. 2 mine were used as the research object; the maximum reuse rate of mine water and the system operation rate comprised the objective function; and mine water quality information, mine water standard, and mine water treatment speed were the constraints. A multi-objective optimization scheduling mathematical model of water supply system was established. Then, to address the problems of premature convergence and ease of falling into a local optimum in the iterative process of particle swarm optimization, the basic particle swarm optimization was improved. Using detailed simulation research, the superiority of the improved algorithm was verified. Eventually, the mine water grading dispatching strategy proposed in this paper is compared with the traditional dispatching method. The results show that the hierarchical dispatching system can significantly improve the mine water reuse rate and system operating efficiency.

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>


2021 ◽  
Vol 218 ◽  
pp. 18-31
Author(s):  
Douglas F. Surco ◽  
Diogo H. Macowski ◽  
Flávia A.R. Cardoso ◽  
Thelma P.B. Vecchi ◽  
Mauro A.S.S. Ravagnani

Sign in / Sign up

Export Citation Format

Share Document