Genetic algorithm for seismic topology optimization of lifeline network systems

Abstract

SUMMARYLifeline systems, such as water distribution and gas supply networks, usually cover large areas. For these systems, seismic design is always a difficult problem because of the complexity of large-scale networks. In this paper, a topology optimization technology for lifeline networks is established. Firstly, in order to speed up the convergence of optimization process, an element investment importance analysis is carried out to evaluate the importance of components to the lifeline network. Then a topology optimization model is established. The aim of the model is to find the least-cost network topology while the seismic reliability between the sources and each terminal satisfies prescribed reliability constraints. For this optimization problem, a genetic algorithm, which takes network topologies as the individuals of its population, is used to search for the optimal solutions by suitable operators, including selection, crossover and mutation operators. The capacity of the proposed algorithm is illustrated by its applications to a simple example network consisting of 10 nodes and an actual network with 391 nodes located in a large city of China.

Comparison of algorithms for seismic topology optimisation of lifeline networks

“…Strengthening the searching ability is a good way to overcome the Structure and Infrastructure Engineering 1359 deficiency. As SAA is capable of widening the searching space and its operator is very similar to the mutation operator in GA, by replacing the mutation operator in GA with the perturbation in SAA, a hybrid algorithm, the socalled SAGA, can be developed to strengthen the searching ability of GA , 2009Liu et al, 2012a). Therefore, each gene generated by the crossover operator will be perturbed to produce several new solutions and updated according to the acceptance probability in Equation (2).…”

“…Based on the seismic connectivity reliability analysis of lifeline networks, Bao (2004) and Liu and Li (2008) presented a simulated annealing algorithm (SAA) to find a least-cost network, of which the seismic reliability satisfies a prescribed value. Furthermore, Li, Liu, and Bao (2008) proposed a topology optimisation model based on the seismic connectivity reliability analysis of lifeline networks and adopted a genetic algorithm (GA) to solve this optimisation problem. Along the same lines, a simulated annealing GA was implemented to solve this problem , 2009.…”

“…First, a topology optimisation model for the lifeline networks subjected to earthquake is introduced by taking the system cost as the optimisation objective and the system seismic reliability as the constraint. Second, three optimisation algorithms, a GA , an SAA (Bao, 2004;) and a simulated annealing genetic algorithm (SAGA; , 2009), a hybrid algorithm that combines the advantages of GA and SAA are introduced to solve the problem. In addition, two recently developed optimisation algorithms, an ACA and a PSA, are introduced for the problem.…”

“…Furthermore, Li, Liu, and Bao (2008) proposed a topology optimisation model based on the seismic connectivity reliability analysis of lifeline networks and adopted a genetic algorithm (GA) to solve this optimisation problem. Along the same lines, a simulated annealing GA was implemented to solve this problem , 2009. For a water distribution network, seismic functional reliability analysis can better describe its state after an earthquake.…”

Research on Sensor Placement for Disaster Prevention in Water Distribution Networks for Important Users

“…The protection and monitoring of important nodes or pipelines are important aspects of improving the resilience of the lifeline network. Jie Li et al [2] introduced an element investment importance analysis in the seismic topology optimization research of the lifeline network, and analyzed the nodes and lines of high importance in the lifeline network in order to speed up the convergence of optimization processes. Considering node importance and power generation recovery capability, Can Zhang et al [3] proposed a two-stage grid reconfiguration strategy, which involves a node importance evaluation method based on the concept of regret.…”