Development of a distributed artificial fish swarm algorithm to optimize pumps working in parallel mode

The existing control systems for the parallel pumps in HVAC systems are mostly based on a centralized structure in which all the data from sensors and control instructions are processed by a master controller. This structure has somehow limited the stability, expandability, and the universality of the control system. The strategies are always case by case, ones which cannot be easily transplanted to other systems and when some devices are out-of-order the entire control system will be influenced. In the current article, a distributed control structure has been proposed for the parallel pumps in which the master controller is replaced by several independent distributed controllers cooperating together to handle the global task. An evolutionary algorithm artificial fish swarm algorithm has been transplanted to the proposed structure to optimize the pumps working in parallel mode. Simulation studies have been conducted based on the models of a physical pump system and the performance of the proposed algorithm is compared with other common heuristic algorithms in both effect and stability. The results show that distributed artificial fish swarm algorithm has inherited the characters of artificial fish swarm algorithm and it is more efficient in the calculation.

[1]  Andrew Kusiak,et al.  Minimizing pump energy in a wastewater processing plant , 2012 .

[2]  Ramiro H. Bravo,et al.  Designing HVAC systems using particle swarm optimization , 2011 .

[3]  İsmail Yabanova,et al.  Thermal monitoring and optimization of geothermal district heating systems using artificial neural network: A case study , 2012 .

[4]  Shuzong Wang,et al.  A Hybrid of Artificial Fish Swarm Algorithm and Particle Swarm Optimization for Feedforward Neural Network Training , 2007 .

[5]  Jr-Shian Chen,et al.  An enhanced genetic algorithm for bi-objective pump scheduling in water supply , 2009, Expert Syst. Appl..

[6]  S. Umashankar,et al.  A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system , 2016 .

[7]  Yi Jiang,et al.  A decentralized algorithm for optimal distribution in HVAC systems , 2016 .

[8]  Pawel Olszewski,et al.  Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps , 2016 .

[9]  Paul Waide,et al.  Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems , 2011 .

[10]  Yanbin Gao,et al.  Optimal artificial fish swarm algorithm for the field calibration on marine navigation , 2014 .

[11]  Mehdi Sargolzaei,et al.  A Review of Artificial Fish Swarm Optimization Methods and Applications , 2012 .

[12]  Khairuddin Omar,et al.  A review of multi-agent systems in building multiple soccer-playing intelligent robots , 2011, 2011 International Conference on Pattern Analysis and Intelligence Robotics.

[13]  Saeed Farzi.  Efficient Job Scheduling in Grid Computing with Modified Artificial Fish Swarm Algorithm , 2009 .

[14]  Yongli Yang,et al.  Research on the route optimization for fresh air processing of air handling unit in spacecraft launching site , 2015 .

[15]  Shantanu Chakraborty,et al.  Multi-Agent based Distributed Optimization Architecture for Energy-Management Systems with Physically Coupled Dynamic Subsystems , 2015 .

[16]  Xiaodan Zhang,et al.  A kind of Composite Shuffled Frog Leaping Algorithm , 2010, 2010 Sixth International Conference on Natural Computation.