Eui Hoon Lee

Stochastic Multiobjective Optimization Model for Urban Drainage Network Rehabilitation

AbstractFlooding in urban areas has become increasingly common in recent decades, as a result of increased urbanization, decreased infiltration rates, and climate change. Hydraulic rehabilitation plans can be developed and implemented to maintain suitable urban drainage system performance. Determining the effective plans, however, requires the involvement of rainfall uncertainties in the modeling and using special tools. In this study, statistical copula functions are established to determine joint probability distribution of rainfall variables considering their dependence structure. The most credible distribution is then used through the Monte Carlo simulation (MCS) to investigate rainfall uncertainties. A multiobjective optimization model is also developed and, after validation, is linked to the EPA-SWMM model for evaluating urban drainage rehabilitation scenarios. The copula-based multiobjective optimization model represents a range of cost-effective rehabilitation plans in terms of overflow improvemen...

Investigating the impact of proactive pump operation and capacity expansion on urban drainage system resilience

AbstractThe individual and combined effects of structural and nonstructural measures on urban drainage system resilience were investigated in this study. The resilience of an urban drainage system ...

Development of Operation in Urban Offline Detention Reservoirs

Recently, the ratio of impervious area, the number of extreme rainfall and runoff in urban areas are dramatically increasing. Because of this reasons, the number of flood disasters are also increasing and there are several measures such as structural measures and nonstructural measures. In this research, operation of drainage facilities among non-structural measures is focused. Operation in urban offline detention reservoirs among drainage facilities was studied. Operation obtaining additional capacity in detention reservoir by storage and discharge considering the status of sewer network was suggested. First flooded node in sewer network was selected as a monitoring point using probability rainfall data and operation of offline detention reservoir was determined according to the level of monitoring point. For verifying effects of new operation, rainfall data in 2010 and 2011 were applied to. In the case of 2010, maximum flood reducing effect of new operation was 28.52% and in the case of 2011, maximum flood reducing effect of new operation was 16.81%.

Development of the new meta-heuristic optimization algorithm inspired by a vision correction procedure: Vision Correction Algorithm

In this study, a new meta-heuristic optimization algorithm, Vision Correction Algorithm (VCA), designed according to the optical properties of glasses was developed. The VCA is a technique applying optometry and vision correction procedure to optimization algorithm through the process of myopic/hyperopic correction-brightness adjustment-compression enforcement-astigmatism adjustment. The proposed VCA unlike the conventional meta-heuristic algorithm is an automatically adjusting global/local search rate and global search direction based on accumulated optimization results. The proposed algorithm was applied to the representative optimization problem (mathematical and engineering problem) and results of the application are compared with that of the present algorithms.

KU Battle of Metaheuristic Optimization Algorithms 1: Development of Six New/Improved Algorithms

Each of six members of hydrosystem laboratory in Korea University (KU) invented either a new metaheuristic optimization algorithm or an improved version of some optimization methods as a class project for the fall semester 2014. The objective of the project was to help students understand the characteristics of metaheuristic optimization algorithms and invent an algorithm themselves focusing those regarding convergence, diversification, and intensification. Six newly developed/improved metaheuristic algorithms are Cancer Treatment Algorithm (CTA), Extraordinary Particle Swarm Optimization (EPSO), Improved Cluster HS (ICHS), Multi-Layered HS (MLHS), Sheep Shepherding Algorithm (SSA), and Vision Correction Algorithm (VCA). This paper describes the details of the six developed/improved algorithms. In a follow-up companion paper, the six algorithms are demonstrated and compared through well-known benchmark functions and a real-life engineering problem.

Performance Comparison of Metaheuristic Optimization Algorithms Using Water Distribution System Design Benchmarks

Various metaheuristic optimization algorithms are being developed and applied to find optimal solutions of real-world problems. Engineering benchmark problems have been often used for the performance comparison among metaheuristic algorithms, and water distribution system (WDS) design problem is one of the widely used benchmarks. However, only few traditional WDS design problems have been considered in the research community. Thus, it is very challenging to identify an algorithm’s better performance over other algorithms with such limited set of traditional benchmark problems of unknown characteristics. This study proposes an approach to generate WDS design benchmarks by changing five problem characteristic factors which are used to compare the performance of metaheuristic algorithms. Obtained optimization results show that WDS design benchmark problems generated with specific characteristic under control help identify the strength and weakness of reported algorithms. Finally, guidelines on the selection of a proper algorithm for WDS design problems are derived.

Multi-layered Harmony Search Algorithm: Introduction of a Novel and Efficient Structure

The harmony search algorithm (HSA) is one of the most widely used meta-heuristic optimization algorithms. During the last two decades, many improved versions and variants of HSA have been proposed to improve the algorithms efficiency and usability. In this study, a HSA variant with unique structural characteristics is proposed, named multi-layered harmony search algorithm (MLHSA). The multi-layered structure is specifically designed for the effective improvement of exploration and exploitation capability. The proposed MLHSA is applied to a set of benchmark problems to test and verify the efficiency. The application results show that MLHSA outperforms other meta-heuristic algorithms, indicating the competitiveness of the algorithm. The multi-layer concept can be easily employed to other algorithms, as a helpful tool for the improvement of existing algorithms convergence.

Development and Applications of Multi-layered Harmony Search Algorithm for Improving Optimization Efficiency

The Harmony Search Algorithm (HSA) is one of the recently developed metaheuristic optimization algorithms. Since the development of HSA, it has been applied by many researchers from various fields. The increasing complexity of problems has created enormous challenges for the current technique, and improved techniques of optimization algorithms are required. In this study, to improve the HSA in terms of a structural setting, a new HSA that has structural characteristics, called the Multi-layered Harmony Search Algorithm (MLHSA) was proposed. In this new method, the structural characteristics were added to HSA to improve the exploration and exploitation capability. In addition, the MLHSA was applied to optimization problems, including unconstrained benchmark functions and water distribution system pipe diameter design problems to verify the efficiency and applicability of the proposed algorithm. The results revealed the strength of MLHSA and its competitiveness.

Determination of operating offline detention reservoir considering system resilience

Recently, the number of occurrences of inundation and the severity of flood damage has increased rapidly as the frequency of localized heavy rainfall and the ratio of impervious area increased in urban areas. Most local governments focus on employing structural measures (e.g., the construction of detention reservoirs/pump stations, rehabilitation of drainage and sewer pipes) to prevent urban inundation. On the other hand, the effectiveness of implementing such structural measures is being dimished because there are already many inundation prevention facilities. The limitation of structural measures can be overcoming by employing non-structure measures, such as flood alerts and the operation of drainage facilities. This study suggests the pump operation rule (i.e., suggesting pump stop level) for a new detention reservoir operating method, which triggers the operation of a pump based on the water level at the monitoring node in urban drainage system. In the new reservoir operation, a total of 48 rainfall events are generated by the Huff distribution for determining the proper pump stop level. First, the generated rainfall events are distributed as frequencies, quartiles, and durations. The averaged system resilience value was determined to range from 1.2 m to 1.5 m is based on the rainfall-runoff simulation with rainfall generated by the Huff distribution. In this range, 1.2 m was identified considering the safety factor of 1.25 by the Standard on sewer facilities in 2011.

KU Battle of Metaheuristic Optimization Algorithms 2: Performance Test

In the previous companion paper, six new/improved metaheuristic optimization algorithms developed by members of Hydrosystem laboratory in Korea University (KU) are introduced. The six algorithms are Cancer Treatment Algorithm (CTA), Extraordinary Particle Swarm Optimization (EPSO), Improved Cluster HS (ICHS), Multi-Layered HS (MLHS), Sheep Shepherding Algorithm (SSA), and Vision Correction Algorithm (VCA). The six algorithms are tested and compared through six well-known unconstrained benchmark functions and a pipe sizing problem of water distribution network. Performance measures such as mean, best, and worst solutions (under given maximum number of function evaluations) are used for the comparison. Optimization results are obtained from thirty independent optimization trials. Obtained Results show that some of the newly developed/improved algorithms show superior performance with respect to mean, best, and worst solutions when compared to other existing algorithms.