김중훈 교수

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows

Inspired by the observation of the water cycle process and movements of rivers and streams toward the sea, a population-based metaheuristic algorithm, the water cycle algorithm (WCA) has recently been proposed. Lately, an increasing number of WCA applications have appeared and the WCA has been utilized in different optimization fields. This paper provides detailed open source code for the WCA, of which the performance and efficiency has been demonstrated for solving optimization problems. The WC

This paper describes a new methodology that can select the pipes to be rehabilitated and/or replaced in an existing water‐distribution system and determine the increase in pumping capacities so that the water demand and pressure requirements at all demand nodes are satisfied while the total rehabilitation and energy cost is minimized. Four cost functions are considered: pipe replacement cost, pipe rehabilitation cost, pipe repair cost, and pumping cost. The methodology considers the trade‐offs a

Since the Harmony Search Algorithm (HSA) was first introduced in 2001, it has drawn a world-wide attention mainly because of its balanced combination of exploration and exploitation and ease of application. The HSA, inspired by musical performance process, consists of three operators: random search, harmony memory considering rule, and pitch adjusting rule. The ways of handling exploration and exploitation with the three operators make the HSA a unique metaheuristic algorithm. However, a series

A new seismic reliability evaluation model is proposed that quantifies the impact of earthquakes on hydraulic behavior of water supply networks. Probabilistic seismic events are produced in the target areas, and the depth of earthquake failure is evaluated by seismic reliability indicators. The developed model was applied to several case studies and used for an intensive examination on how a water supply system hydraulically responds to a seismic event and what system characteristics influence t

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously pub

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is consi

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms