Herein, a novel network partitioning algorithm designed for 1D water drainage networks, but valid for all DAGs, is presented. Additionally, a comparison between partitioning algorithms is ambiguous partition set quality remains undefined for drainage networks whose parallel simulation complexity scales on the total length of the system and number of inter-processor communications per time-step. directed, acyclic graphs (DAGs) with non-power law connectivity, imbalanced link weights, etc. However, water drainage networks have received limited study in the context of network partitioning existing strategies may not address the particular characteristics of 1D water drainage networks, i.e. Myriad heuristic strategies exist for achieving a valid network partition set, as proving an optimal set is formally intractable. Parallelized finite-difference/volume simulation of 1D networks implies a solution to the network partitioning problem in Graph Theory. Water drainage modeling codes need to be updated with parallel simulation capability to leverage modern computational trends.
Experiments on both synthetic benchmark networks and a real-world network show that the proposed algorithm has outperformed the state-of-the-art metaheuristic algorithms.
Two new local search algorithms are proposed to further improve the quality of the solution. To search effectively the huge search space, we propose an improved level-based learning optimizer and use it in both the exploration and exploitation stages. Then, it searches thoroughly in the promising region to obtain the final solution in the exploitation stage. It first finds a promising region of the search space in the exploration stage. To address the issues caused by the large-scale and multimodal characteristics of the problem, the proposed algorithm divides the optimization process into an exploration stage and an exploitation stage. To solve the large-scale WDN optimization problem effectively, a two-stage swarm optimizer with local search (TSOL) is proposed in this article. However, due to the city expansion, the network scale grows at a fast speed so that the efficacy of many current EC algorithms degrades rapidly. Experimental results on a widely-used real network and the benchmark networks show that the proposed algorithm is promising.Įvolutionary computation (EC) algorithms have been successfully applied to the small-scale water distribution network (WDN) optimization problem. Finally, a new suite of benchmark networks are created with both balanced and imbalanced cases. The function transforms the constraint into a part of the objective to punish the infeasible solutions. Second, a new fitness function is devised to handle the pressure constraint of the problem. Meanwhile, during optimization, the proposed algorithm can gradually refine the decomposition to make it more accurate. Since little domain knowledge is required, the decomposition method has great adaptability to multiform networks. First, an iterative trace-based decomposition method is specially designed by utilizing the information of water tracing to divide a large-scale network into small sub-networks. In order to solve the large-scale water distribution network optimization problem, a cooperative co-evolutionary algorithm is proposed in this paper. Nowadays, the scale of the water distribution network of a city grows dramatically along with the city expansion, which brings heavy pressure to its optimal design. A good design of the network can not only reduce the construction expenditure but also provide reliable service. Potable water distribution networks are important infrastructures of modern cities.
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