Competent Genetic-Evolutionary Optimization of Water Distribution Systems

Abstract

A genetic algorithm (GA) has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms  a messy genetic algorithm to enhance the efficiency of an optimization procedure. Maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms.

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

“…A Messy GA, on the other hand, uses variable-length strings that combine short, well-tested building blocks to form longer, more complex strings that increasingly cover all features of a problem under investigation. Examples of Messy GAs adapted and used for the design of water distribution networks are presented by Halhal et al (1997), Walters et al (1999) and Wu and Simpson (2001).…”

A diversity-enriched variant of discrete PSO applied to the design of water distribution networks

“…The complete setting can be found in (Wu and Simpson, 2001). The second case is the New York Tunnel water supply network (Figure 2), which similarly to the Hanoi water distribution problem, has been studied extensively by various researchers (Savic and Walters, 1995;Maier et al, 2003;Matías, 2003).…”

“…For the last decade, many researchers in the field have shifted direction, leaving aside traditional optimization techniques based on linear and nonlinear programming and embarking on the implementation of Evolutionary Algorithms: Genetic Algorithms (Savic and Walters, 1995;Wu and Simpson, 2001;Matías, 2003;Wu and Walski, 2005); Ant Colony Optimization (Maier et al, 2003;Zecchin et al, 2005); Simulated Annealing (Cunha and Sousa, 1999); Shuffled Complex Evolution (Liong and Atiquzzaman, 2004);and Harmony Search (Geem, 2006), amongst others.…”

Diseño óptimo de sistemas de distribución de agua mediante Agent Swarm Optimization

“…Durante la última década, muchos investigadores han empezado a hacer uso de modernas técnicas evolutivas de optimización, dejando atrás otros métodos más tradicionales basados en la programación lineal y no lineal. Refiriéndonos exclusivamente al campo del agua, los algoritmos genéticos han sido los más utilizados (Savic y Walters, 1997;Wu y Simpson, 2001;Matías, 2003;Wu y Walski, 2005), aunque también han sido incorporadas otras técnicas, como las basadas en las colonias de hormigas (ACO, Ant Colony Optimization) (Zecchin et al, 2006;Montalvo et al, 2007a); Simulated Annealing, también denominada 'recocido simulado' (Cunha y Sousa, 1999); Shuffled Complex Evolution (Liong y Atiquzzama, 2004); Harmony Search o búsqueda de la armonía (Geem, 2006); Particle Swarm Optimization (PSO), basada en la inteligencia colectiva de los sistemas de partículas, Montalvo et al, 2008e). Entre las ventajas que han propiciado el uso creciente de los algoritmos evolutivos en el diseño óptimo de SDA, pueden citarse las siguientes:…”

“…During the last decade, many researchers have started using modern evolutionary optimisation techniques, leaving aside more traditional methods based on linear and nonlinear programming. In the field of water systems, genetic algorithms have been the most used (Savic y Walters, 1997;Wu y Simpson, 2001;Matías, 2003;Wu y Walski, 2005), although other techniques based on ant colonies (ant colony optimisation or ACO) (Zecchin et al, 2006;Montalvo et al, 2007a) have been used; as well as simulated annealing (Cunha y Sousa, 1999); shuffled complex evolution (Liong y Atiquzzama, 2004); harmony search (Geem, 2006); and particle swarm optimisation (PSO) based on the collective intelligence of systems of particles Montalvo et al, 2008e). The advantages of the growing use of evolutionary algorithms in the optimal design of WDS include:…”