A novel hybridized grey wolf optimzation for a cost optimal design of water distribution network
- Resource Type
- Conference
- Authors
- Sankaranarayanan, S.; Swaminathan, G.; Sivakumaran, N.; Radhakrishnan, T. K.
- Source
- 2017 Computing Conference Computing Conference, 2017. :961-970 Jul, 2017
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Algorithm design and analysis
Optimization
Kalman filters
Heuristic algorithms
Convergence
Prediction algorithms
Nickel
hybridization
Grey Wolf Optimizer
Particle Swarm Optimization
Kalman Bucy
Water Distribution Network
Optimal cost design
- Language
In this work, a novel Hybridized Grey Wolf Optimizer (HGWO) is proposed by combining the nature inspired Grey Wolf Optimizer (GWO) and Kalman Bucy (KB) correction mechanism. The modifications are implemented in such a way that, the predicted solution obtained from the existing GWO are corrected through the KB mechanism. This improves the efficiency of searching and the rate of convergence towards the global optimum. The local optima avoidance is carried out by the implementation of Shuttling Back and forth Avoidance (SBA) algorithm. An analytical based procedure is followed to determine the learning rate of Kalman gain. The capability of exploring and exploiting the global solution of the HGWO are validated through 6 different benchmark functions. The statistical analysis over the obtained simulation results provides the significance of the HGWO algorithm. The proposed algorithm is applied to obtain a cost optimal design for two different case studies of Water Distribution Network (WDN). The results of HGWO are verified through a comparative study with well-known swarm based PSO and existing GWO algorithm.