Heuristic Harmony Search Research Articles

A Hybrid Heuristic Harmony Search Algorithm for the Vehicle Routing Problem With Time Windows

A Hybrid Heuristic Harmony Search Algorithm for the Vehicle Routing Problem With Time Windows

An Integrated Simulation-Optimization Approach for Dynamic Design of the Urban Wastewater Collection Systems

In this study, a simulation-optimization approach is proposed for dynamic design of the urban wastewater collection systems. The proposed approach consists of the mutual integration of the Storm Water Management Model (SWMM), developed by the U.S. Environmental Protection Agency (US-EPA) and the heuristic Harmony Search (HS) optimization approach. Unlike the previous approaches developed to solve wastewater collection system design problems, the proposed approach simulates the hydraulic flow process by considering unsteady state flow conditions based on the dynamic wave model. The objective of the HS-based optimization model is to determine the pipe slopes so that the total system cost is minimized. After determining the pipe slopes, the corresponding pipe diameters were determined by developing an internal solution approach. All the physical and managerial constraints that should be considered during the search process have been considered by means of the penalty function approach. The applicability of the proposed approach was evaluated by solving two example problems. Identified results indicated that the proposed approach can effectively solve the dynamic design problems of the wastewater collection systems.

A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model

In this study, a novel fully hybrid simulation-optimization approach is proposed for enhancing the prediction performance of the conceptual Technische Universität Wien (TUW) hydrological model. In the simulation part of the proposed approach, the TUW model is hybridized with a support vector regression (SVR) model where SVR directly uses the TUW model outputs as input and simulates the runoff process. This hybridized simulation model is then integrated to a hybrid optimization approach where heuristic harmony search (HS) and Nelder-Mead Simplex (NMS) optimization approaches are mutually integrated. The objective of the hybrid HS-NMS optimization approach is to calibrate the associated parameter values of both TUW and SVR models by maximizing the Nash-Sutcliffe efficiency (NSE) calculated between simulated and observed runoff values. The applicability of the proposed approach is evaluated on a Murat sub-basin of the Gediz River Basin (GRB) in Turkey for different simulation and optimization model combinations. Identified results indicated that the proposed fully hybrid simulation–optimization approach not only efficiently calibrates the associated solution parameters, but also improves the prediction performance of the TUW model.

Predicting Sediment transport in sewers using integrative harmony search-ANN model and factor analysis

This study evaluates the performance of an integrated version of artificial neural network namely HS-ANN (which is a combination of neural network and heuristic harmony search algorithm) as an alternative approach to predict the sediment transport in terms of sediment volumetric concentration (Cv) in sewer pipe systems. To overcome the complexities of choosing the optimum number of the input variables and to consider the effective parameters of the model, the factor analysis technique is utilized. In addition to the HS-ANN model, an empirical equation, as well as a multiple linear regression model, are also considered. The mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficients (PCC) are used for evaluating the accuracy of the applied models. As the comparisons demonstrate, the HS-ANN model (PCC = 0.97) is more accurate than the existing empirical equation and MLR model and could be successfully employed in predicting sediment transport in sewer networks.

Scaled and unscaled ground motion sets for uni-directional and bi-directional dynamic analysis

In this study, solution models are proposed to obtain code-compatible ground motion record sets which can be used for both uni-directional and bi-directional dynamic analyses. Besides scaled, unscaled ground motion record sets are obtained to show the utility and efficiency of the solution models. For scaled ground motion sets the proposed model is based on hybrid HS-Solver which integrates heuristic harmony search (HS) algorithm with the spreadsheet Solver add-in. For unscaled ground motion sets HS based solution model is proposed. Design spectra defined in Eurocode-8 for different soil types are selected as target spectra. The European Strong Motion Database is used to get ground motion record sets. Also, a sensitivity analysis is conducted to evaluate the effect of different HS solution parameters on the solution accuracy. Results show that the proposed solution models can be regarded as efficient ways to develop scaled and unscaled ground motion sets compatible with code-based design spectra.

HS 알고리즘을 이용한 계단응답으로부터 FOPDT 모델 인식

This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

Reconfiguration with Simultaneous DG installation to Improve the Voltage Profile in Distribution Network using Harmony Search Algorithm

Due to dynamic nature of loads, total system load is more than its generation capacity that makes relieving of load on the feeders not possible and hence voltage profile of the system will not be improved to the required level. In order to meet required level of load demand, Reconfiguration DG units are integrated in distribution network to improve voltage profile, to provide reliable and uninterrupted power supply and also to achieve economic benefits such as minimum power loss, energy efficiency and load leveling. This work proposes minimization of real power losses and improvement of voltage profile using network reconfiguration in the presence of distributed generation. Generally distributed generations (DG) are preferred with objective of minimizing real power loss and improving voltage profile in distribution system. In this work A meta heuristic Harmony Search Algorithm (HSA) is used to simultaneously reconfigure and identify the optimal locations for installation of DG units in a distribution network. Sensitivity analysis is used to identify optimal locations for installation of DG units. The proposed method has tested in MATLAB for 33-bus and 69- Bus radial distribution systems at three different load levels and the analysis is presented for loss minimization.

Design of Robust PID Power System Stabilizer for Multimachine Power System Using HS Algorithm

In this paper, the design of a proportional, derivative and integral (PID) based power system stabilizer (PSS) is carried out using a new Meta heuristic harmony search algorithm (HSA) to optimize the parameters. The design of proposed PID controller is considered with an objective function based on eigenvalue shifting to guarantee the stability of nonlinear plant for a wide range of conditions using HSA. The HSA optimized PIDPSS (HSPIDPSS) controller is applied to the standard IEEE ten-machine thirty nine-bus test power system model in decentralized manner and the performance is compared with a robust fuzzy controller. The robustness is tested by considering four plant conditions with change of active power, active load and faults at different buses of the power system to establish, the superior performance with HSPIDPSS over the FPSS.

An almost-parameter-free harmony search algorithm for groundwater pollution source identification

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

Feature Selection Inspired Classifier Ensemble Reduction

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets.

Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation

This paper presents a new method to solve the network reconfiguration problem in the presence of distributed generation (DG) with an objective of minimizing real power loss and improving voltage profile in distribution system. A meta heuristic Harmony Search Algorithm (HSA) is used to simultaneously reconfigure and identify the optimal locations for installation of DG units in a distribution network. Sensitivity analysis is used to identify optimal location s for installation of DG units. Different scenarios of DG placement and reconfiguration of network are considered to study the performance of the proposed method. The constraints of voltage and branch current carrying capacity are included in the evaluation of the objective function. The method has been tested on 33-bus and 69-bus radial distribution systems at three different load levels to demonstrate the performance and effectiveness of the proposed method. The results obtained are encouraging.

A groundwater management tool for solving the pumping cost minimization problem for the Tahtali watershed (Izmir-Turkey) using hybrid HS-Solver optimization algorithm

Summary This study proposes a linked simulation–optimization model to solve the groundwater pumping cost minimization problem for existing and new wells to satisfy any given water demand. The proposed model integrates MODFLOW-2000 with HS-Solver which is a recently proposed global–local hybrid optimization algorithm that integrates heuristic harmony search (HS) algorithm with the spreadsheet Solver add-in. Using the proposed model, a pumping cost minimization problem is solved for different number of wells by considering the pumping rates as well as the locations of additional new wells as the decision variables. Some physical and managerial constraints are defined for this problem. These constraints that need to be satisfied in the optimization process are set up using the penalty function approach. The performance of the proposed model is evaluated on the groundwater flow model of the Tahtali watershed (Izmir-Turkey), an urban watershed which is a key component of Izmir’s water supply system. Also, a sensitivity analysis is performed to evaluate the model results for different sets of HS solution parameters. Results indicate that the proposed simulation–optimization model is found to be efficient in identifying the optimal numbers, locations, and pumping rates of the pumping wells for satisfying the given constraints. Results also show that the model is not only capable of obtaining just any mathematically plausible solution but a realistic one that can be confirmed by repetitive runs of the model.

Centralized and distributed spectrum channel assignment in cognitive wireless networks: A Harmony Search approach

This paper gravitates on the spectrum channel allocation problem where each compounding node of a cognitive radio network is assigned a frequency channel for transmission over a given outgoing link, based on optimizing an overall network performance metric dependant on the level of interference among nearby nodes. In this context, genetically inspired algorithms have been extensively used so far for solving this optimization problem in a computationally efficient manner. This work extends previous preliminary research carried out by the authors on the application of the heuristic Harmony Search (HS) algorithm to this scenario by presenting further results and derivations on both HS-based centralized and distributed spectrum allocation techniques. Among such advances, a novel adaptive island-like distributed allocation procedure is presented, which dramatically decreases the transmission rate required for exchanging control traffic among nodes at a quantifiable yet negligible performance penalty. Extensive simulation results executed over networks of increasing size verify, on one hand, that our proposed technique achieves near-optimum spectral channel assignments at a low computational complexity. On the other hand, the obtained results assess that HS vastly outperforms genetically inspired allocation algorithms for the set of simulated scenarios. Finally, the proposed adaptive distributed allocation approach is shown to attain a control traffic bandwidth saving of more than 90% with respect to the naive implementation of a HS-based island allocation procedure.

A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems

This study proposes a linked simulation–optimization model for solving the unknown groundwater pollution source identification problems. In the proposed model, MODFLOW and MT3DMS packages are used to simulate the flow and transport processes in the groundwater system. These models are then integrated with an optimization model which is based on the heuristic harmony search (HS) algorithm. In the proposed simulation–optimization model, the locations and release histories of the pollution sources are treated as the explicit decision variables and determined through the optimization model. Also, an implicit solution procedure is proposed to determine the optimum number of pollution sources which is an advantage of this model. The performance of the proposed model is evaluated on two hypothetical examples for simple and complex aquifer geometries, measurement error conditions, and different HS solution parameter sets. Identified results indicated that the proposed simulation–optimization model is an effective way and may be used to solve the inverse pollution source identification problems.