IMPACT OF FIREFIGHTING CAPACITY ON WATER DISTRIBUTION SYSTEMS DESIGN USING GENETIC ALGORITHM

2019 ◽  
Vol 75 (6) ◽  
pp. II_209-II_218
Author(s):  
Kohei HASEGAWA ◽  
Yasuhiro ARAI ◽  
Akira KOIZUMI
Keyword(s):  
Genetic Algorithm ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Systems Design

Evolutionary Computation for Single and Multiobjective Water Distribution Systems Optimal Design

2010 ◽  
pp. 332-345
Author(s):  
Avi Ostfeld
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Future Research ◽  
Research Directions ◽  
Fixed Set ◽  
Gradient Type ◽  
Future Research Directions

Water distribution systems least cost pipe sizing/design is probably the most explored problem in water distribution systems optimization. Attracted numerous studies over the last four decades, two main approaches were employed: decomposition in which an “inner” linear programming problem is solved for a fixed set of flows/heads, while the flows/heads are altered at an “outer” problem using a gradient or a sub-gradient type technique; and the utilization of an evolutionary optimization algorithm (e.g., a genetic algorithm). In reality, however, from a broader perspective the design problem is inherently of a multiobjective nature incorporating competing objectives such as minimizing cost versus maximizing reliability. This chapter reviews some of the literature on single and multiobjective optimal design of water distribution systems and suggests a few future research directions in this area.

Robust booster chlorination in water distribution systems: design and operational perspectives under uncertainty

2019 ◽  
Vol 68 (6) ◽  
pp. 399-410
Author(s):  
Denis Nono ◽  
Innocent Basupi
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Systems Design ◽  
Nsga Ii ◽  
Chlorine Disinfection ◽  
Uncertain Network ◽  
Network Operations ◽  
Booster Chlorination ◽  
Injection Rates

Abstract Booster chlorination designs have been widely based on predefined (deterministic) network conditions and they perform poorly under uncertainty in water distribution systems (WDSs). This paper presents a scenario-based robust optimisation approach which was developed to obtain booster chlorination designs that withstand uncertain network operations and water demand conditions in the WDSs. An optimisation problem was formulated to minimise mass injection rates and the risk of chlorine disinfection. This problem was solved by a non-dominated sorting genetic algorithm (NSGA-II). The proposed approach was demonstrated using the Phakalane network in Botswana. The results present robust booster chlorination (RBC) designs, which indicate the number of boosters, locations and injection rates in the network. The performance of RBC designs evaluated under uncertainty reveals lower risks of chlorine disinfection compared to deterministic-based designs. The proposed approach obtains booster chlorination designs that respond better to uncertainty in the operations of WDSs.

A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems

2006 ◽  
Author(s):  
Berge Djebedjian ◽  
Ashraf Yaseen ◽  
Magdy Abou Rayan
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Penalty Method ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimization Problem ◽  
Water Distribution Systems ◽  
Building Blocks ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

This paper presents a new adaptive penalty method for genetic algorithms (GA). External penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. The success of the genetic algorithm application to the design of water distribution systems depends on the choice of the penalty function. The optimal design of water distribution systems is a constrained non-linear optimization problem. Constraints (for example, the minimum pressure requirements at the nodes) are generally handled within genetic algorithm optimization by introducing a penalty cost function. The optimal solution is found when the pressures at some nodes are close to the minimum required pressure. The goal of an adaptive penalty function is to change the value of the penalty draw-down coefficient during the search allowing exploration of infeasible regions to find optimal building blocks, while preserving the feasibility of the final solution. In this study, a new penalty coefficient strategy is assumed to increase with the total cost at each generation and inversely with the total number of nodes. The application of the computer program to case studies shows that it finds the least cost in a favorable number of function evaluations if not less than that in previous studies and it is computationally much faster when compared with other studies.

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