Improved Harmony Search Algorithm Research Articles

Partial Transmit Sequence Optimization Using Improved Harmony Search Algorithm for PAPR Reduction in OFDM

Partial Transmit Sequence Optimization Using Improved Harmony Search Algorithm for PAPR Reduction in OFDM

Long-Term Groundwater-Level Forecasting in Shallow and Deep Wells Using Wavelet Neural Networks Trained by an Improved Harmony Search Algorithm

This study proposes a model using wavelet neural networks (WNNs) trained by a novel improved harmony search (IHS) algorithm to forecast daily groundwater level (GWL) in a shallow well and a deep well in Florida and Arkansas, respectively, for 1 year. Statistical characteristics of the GWL time series and the autocorrelation functions were determined first. Measured GWL series were then decomposed into several subseries using wavelet transform, and imposed as input patterns to the proposed model to forecast GWL in both wells. Efficiency of the IHS algorithm was affirmed by comparing its results with those of differential evolution (DE), harmony search (HS), and particle swarm optimization (PSO) training algorithms. Similarly, the efficiency of WNN was verified by comparing its results with those of the radial basis function (RBF) and multilayer perceptron (MLP) networks, all trained by the IHS algorithm. Predictive capability of the models was determined by Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficients (PCC), normalized root-mean-squared error (nRMSE), and normalized mean absolute error (nMAE) indices. Results reflect that the wells have different statistical characteristics in their GWL series. The shallow well, in contrast to the deep one, has both homoscedasticity and stationarity properties in its observed noisy GWL fluctuations. The proposed model performed much better in both wells, with lower errors and higher PCC and NSE values compared to models with alternative training algorithms or network structures. All forecasts were more accurate for the deep well, as opposed to the shallow one, probably because of the highly noisy GWL fluctuations in the latter.

Optimized symmetric partial facegraphs for face recognition in adverse conditions

In this paper, we propose a memetic based framework called Optimized Symmetric Partial Facegraphs (OSPF) to recognize faces prone to adverse conditions such as facial occlusions, expression and illumination variations. Faces are initially segmented into facial components and optimal landmarks are automatically generated by exploiting the bilateral symmetrical property of human faces. The proposed approach combines an improved harmony search algorithm and an intelligent single particle optimizer to take advantage of their global and local search capabilities. Basically, the hybridization version aids to compute the optimal landmarks. These landmarks further serve as the building blocks to intuitively construct the partial facegraphs. The efficiency of the proposed approach has been investigated in addressing the facial occlusion problem when only one exemplar face image per subject is available using comprehensive experimental validations. The proposed approach yields improved recognition rates when compared to recent state-of-the-art techniques.

An improved Harmony Search Algorithm embedded with a novel piecewise opposition based learning algorithm

Harmony Search Algorithm (HS) is one of the most popular, music-inspired meta-heuristic algorithm. Since its conception HS has been used to solve many complex problems. However, this population based algorithm and its variants suffer from slow convergence speed to the globally optimal solutions. Hence they are computationally expensive. Opposition Based Learning Theory, a machine learning algorithm addresses this issue by considering both estimates and counter-estimates i.e. guess and counter-guess of a candidate solution, population and opposite population of a population based algorithm etc. simultaneously. Although this approach shows great promise, the problem of slow convergence rate in Harmony Search Algorithm is still not completely alleviated. We introduce some improvements over the Opposition based Learning Theory to accelerate the convergence rate of such algorithms. The proposed scheme employs a piecewise counter estimate updating technique while computing a candidate solution. In the present work, the proposed Opposition based Learning technique has been embedded in the framework of Harmony Search Algorithm. An exhaustive set of test functions is used in the experimental setup. The results obtained from the experiments are very promising.

Tuned mass damper system of high-rise intake towers optimized by improved harmony search algorithm

The seismic safety and proper functioning of intake towers in a major earthquake are very crucial to the whole hydraulic project, since the controlled release of the reservoir could help to prevent catastrophic failure of a dam after an earthquake by reducing the water pressure. In this study, the tuned mass damper (TMD) system was introduced into the seismic design of high-rise intake towers. The installation of TMD can effectively dissipate the seismic energy acting on the intake tower and thus overcome the whiplash effect of the hoist room. The 3D finite element model of the intake tower was simplified into a 2D MDOF model. Improved harmony search (IHS) algorithm was used to determine the optimal parameters for the TMD systems using Matlab, and the robustness of the TMD systems was investigated. The simulation elements of the TMD system were incorporated into ADINA to simulate and evaluate the effect of the TMD system on the dynamic displacement and stress responses of various key points of the intake tower to different seismic excitations.

Location, Allocation and Routing of Temporary Health Centers in Rural Areas in Crisis, Solved by Improved Harmony Search Algorithm

In this paper, an uncertain integrated model for simultaneously locating temporary health centers in the affected areas, allocating affected areas to these centers, and routing to transport their required good is considered. Health centers can be settled in one of the affected areas or in a place out of them; therefore, the proposed model offers the best relief operation policy when it is possible to supply the goods of affected areas (which are customers of goods) directly or under coverage. Due to that the problem is NP-Hard, to solve the problem in large-scale, a meta-heuristic algorithm based on harmony search algorithm is presented and its performance has been compared with basic harmony search algorithm and neighborhood search algorithm in small and large scale test problems. The results show that the proposed harmony search algorithm has a suitable efficiency.

Optimal Reactive Power Dispatch with Static VAR Compensator Using Harmony Search Algorithms

One of the major optimization problems in power systems is the optimal reactive power dispatch (ORPD) problem. ORPD plays an important role for secure and reliable operation of power systems. The main purpose of ORPD is to find the settings of control variables such as voltage rating of generators, reactive power injection of VAr compensators and tap ratios of the tap setting transformers in order to minimize the total active power losses of the network while satisfying a given set of constraints. The whole ORPD problem is considered as a non-linear multi-modal optimization problem with a combination of discrete and continuous variables. Along with the conventional control variables in ORPD the optimal location and the Var settings of SVC are included in the optimization problem for additional active power loss reduction. In this paper, two versions of harmony search algorithms namely basic Harmony Search Algorithm (HSA) and Improved Harmony Search algorithm (IHSA) are applied to solve conventional ORPD, as well as ORPD with the FACTS device Static Var Compensator (SVC), to minimize the total active power loss of the power system. The harmony search algorithms for ORPD, as well as ORPD considering SVC are implemented on IEEE 14 and 57 bus systems. Comparison of simulation results reveals the effectiveness of harmony search algorithms over other well established population based optimization techniques.

Improved Harmony Search Algorithm: LHS

In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i) adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space; (ii) opposition-based learning technique is blended to increase the diversity of solution; (iii) competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima. The performance of the LHS algorithm with respect to harmony memory size (HMS) and harmony memory considering rate (HMCR) are also analyzed in detail. To further evaluate the performance of the proposed LHS algorithm, comparison with ten state-of-the-art harmony search variants over a large number of benchmark functions with different characteristics is carried out. The numerical results confirm the superiority of the proposed LHS algorithm in terms of accuracy, convergence speed and robustness.

Reactive power coordinated optimisation method with renewable distributed generation based on improved harmony search

The reactive power coordinated optimisation problem for distribution network (DN) with the integration of renewable distributed generation (DG) is investigated in this study. By actively participating in reactive power control together with an under-load tap changer and shunt capacitors, DG can operate more effectively in the DN. Non-dispatchable DG is considered in the coordinated optimisation method. The objective of the proposed reactive power coordinated optimisation method is to minimise active power loss and to decrease the number of switching device operations while maintaining the grid voltage within the allowable range. The optimisation problem is to solve a variable combination including integers and continuous variables, which will formulate a mixed integer non-linear programming (MINLP) problem. The control variable includes operations of traditional reactive power control devices and the reactive power adjustments of DGs. To solve the MINLP problem, a heuristic searching method, an improved harmony search algorithm is proposed with adaptive parameters process. Numerical tests on a standard 10-kV distribution system and a real distribution system demonstrate the applicability of the proposed reactive power coordinated optimisation method.

Designing optimal tuned mass dampers using improved harmony search algorithm

In this article, the optimum parameters of tuned mass dampers for suppressing the dynamic responses of multi degree-of-freedom structures induced by base excitations are proposed. The improved harmony search algorithm, which has been successfully applied in many fields, is used for tuning the tuned mass dampers under seismic loading. The parameters of tuned mass dampers including mass, damping coefficient, spring stiffness, and location are assumed as design variables and two objective functions have been considered. The results of the numerical simulations modeling of two 10-story shear structures show that tuned mass dampers are very effective in the reduction in energy responses of structures under recorded earthquakes. Also, the objective function offered in this article with better uniform distribution in transfer functions is more reliable than those which will be discussed in the following sections. Furthermore, in all earthquakes, the maximum displacement of tuned mass dampers and force exerted by tuned mass dampers on the structure for the offered objective function are considerably less. A discussion on the validity of the model used by Bekdaş and Nigdeli is also presented in detail. The results indicate that for developing reliable preliminary-design criteria, the proposed analysis-based approach presented herein has the potential to provide better calculation of the responses of such structures.

Cuckoo Search Algorithm for Optimal Placement and Sizing of Static Var Compensator in Large-Scale Power Systems

Abstract This paper presents an application of Cuckoo search algorithm to determine optimal location and sizing of Static VAR Compensator. Cuckoo search algorithm is a modern heuristic technique basing Cuckoo species’ parasitic strategy. The Lévy flight has been employed to generate random Cuckoo eggs. Moreover, the objective function is a multiobjective problem, which minimizes loss power, voltage deviation and investment cost of Static VAR Compensator while satisfying other operating constraints in power system. Cuckoo search algorithm is evaluated on three case studies and compared with the Teaching-learning-based optimization, Particle Swarm optimization and Improved Harmony search algorithm. The results show that Cuckoo search algorithm is better than other optimization techniques and its performance is also better.

Superstructure optimization of the olefin separation system by harmony search and genetic algorithms

In this paper a superstructure optimization is applied for the problem of olefin separation system which includes distillation column units, heat exchangers and heat integration. A new algorithm is presented for modeling the separation system and designing the heat integration and heat exchanger networks. The objective is to minimize the total annual cost of the separation superstructure using the presented design methodology and numerical methods such as improved harmony search algorithm. The developed code is validated and verified using standard cases in literature. The solution approach for the superstructure optimization is discussed and the numerical results are presented proving the improvements in the design of the separation superstructure system using the new presented approach.

On Different-Dimensional Deployment Problems of Hybrid VANET-Sensor Networks with QoS Considerations

To enhance vehicular ad hoc networks (VANETs) with the ability to detect road conditions, hybrid VANET-sensor networks (HVSNs) additionally deploy a large number of wireless sensors on road sides, so as to provide a safer driving environment. Most of the previous works on network deployment problems considered too simplified problem settings, and few of them focused on deploying HVSNs. To meet more practical requirements, this paper investigates the problem of deploying roadside units (RSUs) to serve sensors on three different-dimensional road settings (i.e., a one-dimensional road, a road represented as grid points, and a dense network environment) in HVSNs with two objectives (i.e., to minimize the total distance and the total number of hops from sensors to their respective closest RSUs) and two QoS constraints (i.e., for maximal number of hops and capacity). Achieving the two objectives can effectively decrease the probability of transmission interference and lead to shorter transmission time to inform drivers of unexpected events. Since the optimal deployment problems for a grid road and a dense HVSN cannot be solved analytically, this paper creates mathematical programming models for the problems, and further proposes an improved harmony search algorithm. By simulation in different-size instances and statistical analysis, the proposed approaches show promising performance in solving the problems.

Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem

Abstract Harmony Search (HS) is a population based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony and has ability to escape from local minima, does not require differential gradients and initial value setting for the variables and free from divergence and has strong ability on exploring the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a memetic algorithm approach considering Harmony Search and Random Search is presented in the proposed research to solve unit commitment problem of electric power system. The proposed memetic algorithm is tested for IEEE benchmarks consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is tested for unimodal and multimodal benchmark functions and is compared with others well known evolutionary, heuristics and meta-heuristics search algorithms and it has been found that performance of proposed hybrid algorithm is much better than classical Harmony Search Algorithm and Improved Harmony Search Algorithm as well as recently developed algorithms.

Coordinated design of power system stabilizers and TCSC employing improved harmony search algorithm

Power System Stabilizers (PSS) are generally employed to damp electromechanical oscillations by providing auxiliary stabilizing signals to the excitation system of the generators. But it has been found that these Conventional PSS (CPSS) do not provide sufficient damping for inter-area oscillations in multi-machine power systems. Thyristor Controlled Series Capacitor (TCSC) has immense potential in damping of inter-area power swings and in mitigating the sub-synchronous resonance. In this paper Improved Harmony Search Algorithm (IHSA) has been proposed for coordinated design of multiple PSS and TCSC in order to effectively damp the oscillations. The results obtained by using IHSA on WSCC 3-machine, 9-bus system are found to be superior compared to the results obtained using Bacterial Swarm Optimization (BSO) algorithm. The damping performance of conventional PSS and TCSC controllers is also compared with coordinated design of IHSA based PSS and TCSC on New England 10-machine, 39-bus system over wide range of operating conditions and contingencies. To demonstrate the effectiveness of the proposed technique the results obtained on this test system are also compared with the results obtained with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA) and Bacterial Swarm Optimization (BSO).

Hybrid HS–random search algorithm considering ensemble and pitch violation for unit commitment problem

Harmony search (HS) is a population-based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony. The pitch of each musical instrument determines the aesthetic quality, just as the fitness function value determines the quality of decision variables. In the musical improvisation process, all players sound pitches within possible range together to make one harmony. If all the pitches make a good harmony, each player stores in his memory that experience and the possibility of making a good harmony is increased next time. Even though HS has the ability to escape from local minima, it does not require differential gradients and initial value setting for the variables, is free from divergence and has strong ability to explore the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a novel and hybrid version of harmony search combined with random search algorithm is presented in the proposed research to solve single-area unit commitment problem of electric power system. The proposed algorithm is tested for standard IEEE systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and metaheuristics search algorithms, and it has been found that performance of proposed algorithm is much better than of classical harmony search algorithm and improved harmony search algorithm as well as recently developed algorithms. Sensitivity analysis on proposed algorithm shows that low value of pitch adjustment rate results in better cost, and parametric test on proposed algorithm shows the rejection of the null hypothesis at the alpha significance level.

A Modified Harmony Search Algorithm for the Optimum Design of Earth Walls Reinforced with Non-uniform Geosynthetic Layers

Traditional design and construction of reinforced earth walls assumes uniform length and spacing of reinforcements. Even though the assumption simplifies the design and construction efforts, the inherently conservative approaches followed in picking the final values for the reinforcement length and spacing result in unnecessarily big construction costs. This paper presents an improved harmony search-based approach that can be adopted to optimize the design of Geosynthetic-reinforced earth walls. An existing improved harmony search algorithm is modified into a new harmony search algorithm by extending its capabilities to consider permutation-based operations for inter-dependent variables. The involved optimization procedures are discussed in a step-wise approach. This novel approach allows the consideration of non-uniform length and spacing for the reinforcement layers. As such, length of the Geosynthetic reinforcement and the spacing between adjacent Geosynthetic layers are taken as the design variables to be manipulated until the cost of construction is optimized. Static and dynamic loads are considered. the application of the proposed optimization technique is demonstrated on Geosynthetic-reinforced earth walls of height 5, 7 and 9 m. The extent of cost saving is assessed by comparing the results of this work and previous work. The previous work selected for comparison uses harmony search algorithm to optimize the design and construction of Earth Walls reinforced with uniform length- and spacing-Geosynthetic layers. The IHS-based optimization resulted in Cost reduction of up to 11 %.

An improved harmony search algorithm with dynamically varying bandwidth

The present work demonstrates a new variant of the harmony search (HS) algorithm where bandwidth (BW) is one of the deciding factors for the time complexity and the performance of the algorithm. The BW needs to have both explorative and exploitative characteristics. The ideology is to use a large BW to search in the full domain and to adjust the BW dynamically closer to the optimal solution. After trying a series of approaches, a methodology inspired by the functioning of a low-pass filter showed satisfactory results. This approach was implemented in the self-adaptive improved harmony search (SIHS) algorithm and tested on several benchmark functions. Compared to the existing HS algorithm and its variants, SIHS showed better performance on most of the test functions. Thereafter, the algorithm was applied to geometric parameter optimization of a friction stir welding tool.

Assembly sequence planning based on an improved harmony search algorithm

As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability.

Combinatorial Test Generation using Improved Harmony Search Algorithm

Combinatorial testing can effectively detect the faults triggered by interactions among the various factors in software system. Harmony Search Algorithm (HS), which is a new optimization algorithm and has been widely applied in the fields of portfolio design, repeatedly adjusts the solution variables in harmony memory to reach the optimum. In order to improve the convergence speed of HS, we propose an improved HS algorithm (IHS) and uses one-test-at-a-time strategy to generate a set of optimum initial solutions in IHS. To avoid the algorithm falling into local optima, we dynamically adjust the values of HMCR and PAR in the new algorithm. Compared to some existing algorithms and tools, the improved harmony search algorithm performs more stably and efficiently in generating optimum combinatorial test cases.