Assessment of Penalty-Free Multi-Objective Evolutionary Optimization Approach for the Design and Rehabilitation of Water Distribution Systems

Abstract

This paper describes a penalty-free multi-objective evolutionary optimization approach for the phased whole-life design and rehabilitation of water distribution systems. The optimization model considers the initial construction, rehabilitation and upgrading costs. Repairs and pipe failure costs are included. The model also takes into consideration the deterioration over time of both the structural integrity and hydraulic capacity of every pipe. The fitness of each solution is determined from the trade-off between its lifetime costs and its actual hydraulic properties. The hydraulic analysis approach used, known as pressure-dependent modelling, considers explicitly the pressure dependency of the water supply consumers receive. Results for two sample networks in the literature are included that show the algorithm is stable and finds optimal and near-optimal solutions reliably and efficiently. The results also suggest that the evolutionary sampling efficiency is very high. In other words, the number of solutions evolved and analysed on average before finding a near-optimal solution is small in comparison to the total number of feasible and infeasible solutions. We found better solutions than those reported previously in the literature for the two networks considered. For the Kadu network, for example, the new best solution costs Rs125,460,980-a significant improvement. Additional statistics that are based on extensive testing are included.

Penalty-Free Multi-Objective Evolutionary Approach to Optimization of Anytown Water Distribution Network

“…In addition, the optimality of the solution heavily depends on these parameters. This paper extends previous work by the authors that was concerned with the optimization of networks with pipes only (Siew and Tanyimboh 2012a;Siew et al 2014). The development and application of a multi-objective evolutionary optimization approach for the design and rehabilitation of water distribution systems that avoids the above-mentioned difficulties is described.…”

“…Maximizing this criterion aims to satisfy all the nodal demands (Ackley et al 2001). In this way, the minimum node pressure constraints are addressed seamlessly (Siew and Tanyimboh 2012a;Siew et al 2014). …”

“…We used a genetic algorithm PF-MOEA (penalty-free multi-objective evolutionary algorithm) developed in this research (Siew and Tanyimboh 2012a;Siew et al 2014) to solve the optimization problem. PF-MOEA is based on NSGA II (non-dominated sorting genetic algorithm) (Deb et al 2002).…”

“…In other words, non-dominated infeasible solutions are considered superior to dominated feasible solutions. Additional details on the methodology and its effectiveness are available in Siew and Tanyimboh (2012a) and Siew et al (2014). Also, demonstrations of the effectiveness of an alternative penalty-free formulation that uses the EPANET 2 hydraulic simulator are available in Saleh and Tanyimboh (2013.…”

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

“…In this way the constrained optimization problem was converted and solved as an unconstrained problem without introducing any constraint violation penalties as penalty-free genetic algorithms have achieved better results than other algorithms in the literature consistently (Saleh and Tanyimboh 2013;Siew et al 2014Siew et al , 2016.…”