An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization

TL;DR

This paper introduces a new simulation-based iterated local search metaheuristic for optimizing the design of water distribution networks, effectively handling multi-period demand variations and outperforming existing methods in speed and solution quality.

Contribution

The paper presents a novel metaheuristic that enhances water network design optimization, especially for large, dynamic systems, with improved convergence and robustness.

Findings

Outperforms a state-of-the-art metaheuristic in most tests.

Converges faster to low-cost solutions.

Achieves smaller deviations from best known solutions.

Abstract

The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. In this paper, we tackle the NP-hard water distribution network design (WDND) optimization problem in a multi-period setting where time varying demand patterns occur. We propose a new simulation-based iterated local search metaheuristic which further explores the structure of the problem in an attempt to obtain high quality solutions. Computational experiments show that our approach is very competitive as it is able to improve over a state-of-the-art metaheuristic for most of the performed tests. Furthermore, it converges much faster to low cost solutions and demonstrates a more robust performance in that it obtains smaller deviations from the best known solutions.