Application of multi‐objective evolutionary algorithms for the rehabilitation of storm sewer pipe networks

Abstract

In recent decades, evolutionary optimisation algorithms have been used successfully for a wide variety of water resources engineering problems and their applications are still increasing. In this research work, a hybrid harmony search algorithm, "Non-dominated Sorting Harmony Search" algorithm (NSHS) is developed and compared with two state of the art multi-objective evolutionary algorithms -the NSGA-II and MOPSO algorithms -for assigning optimal rehabilitation plans for sewer pipe networks. The algorithms considered were validated using some standard test functions reported in the literature and compared with each other in terms of several metrics. These algorithms were then linked to the SWMM-EPA hydraulic model and applied to a storm sewer pipe network case study in Seoul, South Korea, to obtain the best rehabilitation plans for pipe replacements. The results showed that the algorithms considered have different behaviors in solving the benchmark tests and rehabilitation problem. The proposed hybrid multi-objective harmony search algorithm provides better optimal solutions in terms of different metrics and clearly outperforms the other two algorithms for the rehabilitation of the storm sewer pipe networks.

Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization

“…However, methods based on the concept of return period do not take into account economic issues. It might result in both overdesigned or underdesigned networks [20]. Cost-optimal design of hydraulic network is a complex problem for engineers.…”

Non-Dominated Sorting Harmony Search Differential Evolution (NS-HS-DE): A Hybrid Algorithm for Multi-Objective Design of Water Distribution Networks

“…Several applications of this algorithm have been reported in the water engineering literature and a few multi-objective schemes have recently been developed [27,30] combining HS operators with non-domination sorting (NS) and crowding distance criteria to generate Pareto-optimal solutions in multi-objective problems. This algorithm called NSHS was compared with the NSGA2 and MOPSO algorithms based on several benchmark problems and a sewer pipe network application and the results showed that the NSHS algorithm outperformed the other two.…”

“…It is expected that if this method is combined with a local search optimizer, the performance of the evolutionary search improves. According to a study by Yazdi et al [27], the harmony search performs very well in finding optimal solutions located in certain parts of the search space, but cannot generate adequately diverse Pareto-optimal solutions, thus degrading the overall performance of the search algorithm. To exploit the local search advantages of HS and the exploratory strength of DE, we propose a hybrid HS-DE method here.…”

“…To achieve this, a greater crowding distance is allocated to the solutions with a smaller number of neighbors. Crowding distance is defined as [27]:…”

Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification

“…By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort [2]. There has been a widespread usage of metaheuristics and their applications in artificial intelligence, e.g., transit network design problems [3], sewer pipe networks [4], water distribution systems [5], sizing optimization of truss structures [6], ordinary differential equations [7], and so forth. Using metaheuristic algorithms, complex problems are not far from finding their solutions [7].…”