Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka

This paper brings an in detail Genetic Algorithm (GA) based combinatorial optimization method used for the optimal design of the water distribution network (WDN) of Gurudeniya Service Zone, Sri Lanka. Genetic Algorithm (GA) mimics the survival of the fittest principle of nature to develop a search process. Methodology employs fuzzy combinations of pipe diameters to check their suitability to be considered as the cost effective optimal design solutions. Furthermore, the hydraulic constraints were implicitly evaluated within the GA itself in its aim to reaching the global optimum solution. Upon analysis, the results of this approach delivered agreeable design outputs. In addition, the comparison made between the results obtained by a previous study inspired by the Honey Bee Mating Optimization (HBMO) Algorithm and results obtained by the GA based approach, proves competency of GA for the optimal design of water distribution network in Gurudeniya Service Zone, Sri Lanka.Comment: Under a review of a journal. 20 pages. arXiv admin note: text overlap with arXiv:2209.1199

Novel Spanning-Tree Matrix Approach to Model and Optimize Large-Scale, Tree-Shaped Water Distribution Networks

There exist many criteria for the optimal design of water distribution networks. One of the most common criteria is to design the optimal cost water distribution network while satisfying the hydraulic design constraints. This study was carried out to propose a novel computational method named Spanning-Tree Matrix Approach that can model large-scale tree-shaped water distribution networks. A case study was tested to demonstrate the use of the Spanning-Tree Matrix Approach model coupled with the Honey-Bee Mating Optimization algorithm to find the combination of pipe diameters that minimizes the cost of the network. The results show that the Spanning-Tree Matrix Approach is successful in modeling a tree-shaped water distribution network of any size. Moreover, proposed Spanning-Tree Matrix Approach has the flexibility to be adapted to any desirable governing equation or design criteria being imposed, and the element of simplicity to output desired constraint evaluations into a modern stochastic optimization algorithm (i.e., Genetic Algorithm, Simulated Annealing, Ant-Colony Optimization, Honey-Bee Mating Optimization, etc.) for the network optimization purpose.Comment: Under the review of Journal of Hydraulic Engineering. Submitted on 12th of August, 202

Cost-Sensitive Analysis in Multiple Time Series Prediction

Abstract- In this paper we propose a new methodology for Cost-Benefit analysis in a multiple time series prediction problem. The proposed model is evaluated in a real world application based on a network of wireless sensors distributed in energy production plants in a region. These sensors generate multiple time series data representing energy production. To build the prediction model for total energy production in the region we have used three common forecasting techniques, Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and Multiple Regression (MR). For training and testing of the models we have used the data from year 2002 to 2004. We analyzed the quality of total energy prediction with different subsets of sensors. We build our cost-benefit model for the prediction process as a function of sensors in a distributed network and estimated the optimum number of sensors that will balance the expenses of the system with the prediction accuracy