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Adjacent structures are under to risk of pounding. The pounding of the structures is the reason of the collapse. To prevent the pounding, a seismic gap needs to be provided. If this gap is short, the sway of the structures may be prevented. For this purpose, a vibration absorber system is proposed. The performance of the system is dependent on the perfect tuning of the absorber parameters and the investigation of the proposed system is done for pulse-like impulsive motions found in near fault excitations. Eight different combinations of the single degree of freedom structures were investigated as the numerical examples. Comparing to the recorded ground motions, the proposed absorber system is more effective on impulsive motions. [ABSTRACT FROM AUTHOR]

Based on the characteristics of Kuroshio passing through Green Island, Taiwan, a new type of floating platform has been designed to capture the maximal power of the marine current. The inclusion of nozzle and diffuser in a shrouded duct is frequently used to accelerate, decelerate, or affect flows in a desirable manner. In this study, a shrouded nozzle-diffuser horizontal turbine was designed by Kirke, B, (2005). Matsushima, T et al.(2005). Five geometric parameters are determined to have an optimal power take off (PTO) from the ocean current. The Harmony search technique was used to search for the best combination of the geometric parameters. The parameters needed to be determined are inlet and outlet diameters, the lengths of the nozzle and diffuser and the gap between blade tips and the inner wall of the duct. Ohya, Y et al.(2010). The simulation of the characteristics of flow passing the system and the calculation of the PTO are made by Fluent. The proposed nozzle-diffuser duct and the meshes arrangement and simulated velocity vectors arc illustrated in Fig. 1. The simulated results show the proper nozzle-diffuser design can effectively increase the power take off (PTO) and PTO coefficient can be as large as 0.48 which is beyond the reported average values. [ABSTRACT FROM AUTHOR]

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy. [ABSTRACT FROM AUTHOR]

This work presents efficient constrained optimization methods for sizing of a differential amplifier with current mirror load. The aim is to minimize MOS transistor area using three evolutionary algorithms, differential evolution, artificial bee colony algorithm and harmony search. Simulation results demonstrate that proposed methods not only meets design specifications and accommodates required functionalities but also accomplishes the design objective and improves some design specifications in a shorter computational time with respect to previous method. [ABSTRACT FROM AUTHOR]

In this paper, a Hybrid Harmony Search Algorithm (HHSA) is presented for Nurse Rostering Problem (NRP) using the dataset proposed by the First International Nurse Rostering Competition (INRC2010). NRP is tackled by assigning daily shifts to nurses with different skills and working contracts, subject to hard and soft constraints. Harmony Search Algorithm (HSA) is a recent evolutionary computing technique, mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments. Recently, HSA has been used for NRP, with promising results. This paper extends HSA to HHSA by adding two powerful concepts to HSA: (i) hybridization with hill climbing optimizer to improve the exploitation ability, and (ii) hybridization with global-best concept of particle swarm optimization to improve the speed of convergence. The proposed HHSA is evaluated against a dataset provided by INRC2010. The results show that it is a powerful technique for INRC2010 dataset. A comparative analysis with five competitive methods is conducted. HHSA outperforms the other competitive methods in three instances and obtained the best results in 29 others out of 69 instances. The efficiency of our method lends further support to the previous theory based on hybridizing the local search within evolutionary computing technique for hard combinatorial optimization problems. [ABSTRACT FROM PUBLISHER]

This paper contributes towards modeling for the designing and reengineering of objects in the areas of Computer Graphics (CG), Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). It provides a modeling technique for the designing of objects. The model is based on a conic-like curve (rational quadratics) method and provides an extra degree of freedom to the user to fine tune the shape of the design to the satisfactory level. The 2D curve model has been then been extended for the reengineering of objects to reproduce fancy objects. The scheme has beenorientedtoautomatethedegree of freedom when a reengineering is required for images of the objects. A heuristic technique of genetic algorithm is applied to find optimal values of shape parameters in the description of conics. It has various phases including extracting outlines of images, mining feature points from the detected outlines, and curve fitting. The idea of particle swarm optimization has been incorporated to optimize the shape parameters in the description of the conic spline. The method ultimately produces optimal results for the approximate vectorization of the digital contours obtained from the generic shapes. Demonstrations also make the essential part of the paper. [ABSTRACT FROM AUTHOR]

Harmony search (HS) algorithm is a recent evolutionary algorithm which mimics the musical improvisation process. It has been successfully applied to a wide range of combinatorial optimization problems. Office-Space-Allocation (OSA) is the process of distributing given limited spaces to given resources in accordance with given constraints: hard and soft. Hard constraints are mandatory to satisfy while the soft constraints are desired but not absolutely essential. The main aim is to find a solution with the least violations of soft constraints while the hard constraints are satisfied and the best usage of spaces is achieved. In this paper, HS is modified and adapted for OSA. The modification includes two HS operators: i) memory consideration where the global-best concept of Particle Swarm Optimization is borrowed and employed; and ii) the pitch adjustment has been designed to be as a local search agent with three effective neighbourhood structures. The proposed Modified Harmony Search Algorithm (MHSA) is evaluated using three datasets from Nottingham and Wolverhampton universities. Interestingly, MHSA obtained two new best results, and a competitively comparable result for the third dataset. [ABSTRACT FROM AUTHOR]

The high-dimensional feature vectors often impose a high computational cost when classification is performed. Feature selection plays major role as a pre-processing technique in reducing the dimensionality of the datasets in data analysis and data mining. This process reduces the number of features by removing irrelevant and redundant data and hence resulting in acceptable classification accuracy. Filter and wrapper are the two kinds of feature selection methods. Experimental results have proved that the wrapper methods can yield better performance, although they have the disadvantage of high computational cost. This paper presents a Harmony Search based novel optimization algorithm for wrapper feature selection. 1-NN classifier method has been used to evaluate the quality of the solutions. The performance of the proposed approach has been analysed by experiments with various real-world data sets. The proposed method, HS-1-NN, produced better performance than other state-of-the-art methods in terms of classification accuracy and convergence rate. [ABSTRACT FROM PUBLISHER]

In previous paper, we proposed the novel method of nonlinear unsupervised feature classification for EEG (Electroencephalography) signal based on HS (Harmony Search) algorithm. Using this method, we could convert classification problem into finding the smallest sum of Euclidean distances between vectors belonging to each class. Therefore the performance of proposed method was influenced by the performance of optimization algorithm. In this paper, to compare efficiency and performance of various heuristic algorithm for this method, we applied three different heuristic optimization algorithm, HS, PSO (Particle Swarm Optimization), and DS (Differential Search). For the simulation, we used EEG signal data from BCI Competition IV Dataset I. Two class data from two subject with 100 Hz sampling rate were used. For feature extraction, common spatial pattern algorithm was used. In conclusion, the fastest algorithm was HS algorithm with about 4.4 seconds of an average computational time, the algorithm with best classification rate was also HS algorithm and the average classification rates of subject ‘f’ and ‘g’ were 84.08 % and 81.95 %. The slowest heuristic algorithm was PSO algorithm with about 7.5 second in an average computational time, and the worst average classification rate was 57.27 % from subject ‘g’ with PSO algorithm. We could draw a conclusion that the best algorithm for proposed classification method was HS algorithm. [ABSTRACT FROM PUBLISHER]

The objective of the paper is to reduce the losses and improve the voltage profile of the radial distribution system with optimally placing the multiple distributed generator in the system. In this paper analytical expressions for optimal siting of distributed generators (DG). The optimal sizing of distributed generators by Improved Analytical (IA) method and Harmony search algorithm. The analysis is carried out on IEEE-33 bus and IEEE-69 bus systems. [ABSTRACT FROM PUBLISHER]

Distribution network reconfiguration is a complex, large-scale and nonlinear combinatorial optimization problem. In order to solve this problem, a new method based on harmony search (HS) algorithm and genetic algorithm (GA) is proposed. The HS algorithm is a recently developed meta-heuristic algorithm, and has been very successful in a wide variety of optimization problems. Refresh strategy of solution vectors is so simple that HS algorithm is easy to fall into local optimum solution when complex problem is solved. However, selection, crossover and mutation operations in GA can generate diverse solutions, so premature stagnation behaviour can be avoided in some extent. The probability of update strategy based on HS algorithm for harmony memory is higher during the initial stage in order to accelerate the searching speed, and the probability of update strategy based on GA is higher during the final stage in order to escape from local optimum solution. Calculation results show that the proposed hybrid algorithm is effective and it converges well. [ABSTRACT FROM PUBLISHER]

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community detection problem this study used modified harmony search algorithm (HAS), the original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local Search (CLS). In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiments in synthetic and real networks show that the proposed multi-objective community detection algorithm is able to discover more accurate community structures. [ABSTRACT FROM PUBLISHER]

This paper presents a study for the network reconfiguration problem for loss reduction with distributed generation units (DGs) included in the network. Two heuristic algorithms inspired from natural phenomena are proposed to solve the problem, real ants'-behavior-inspired, Ant Colony Optimization (ACO) implemented in the Hyper Cube (HC) framework and musicians'-behavior-inspired, Harmony Search (HS) algorithm. A 32-bus and 69-bus distribution systems were selected for optimizing the configuration with and without DG units installed. The results of reconfiguration using the proposed algorithms show that both of them yield the optimum configuration with minimum power loss for each case study however, the HS required shorter simulation time but more practice of the iterative process than the HC-ACO. Implementing the ACO in the HC framework resulted in a more robust and easier handling of pheromone trails than the standard ACO. Insertion of DGs in the distribution networks contributed to loss reduction with reasonable percentage. [ABSTRACT FROM PUBLISHER]

A novel type of discrete-time fractional-power nonlinear autoregressive with exogenous input (FPNARX) model is introduced for system identification, modeling and prediction. Parameter estimation of such a model is a nonlinear optimization problem. A harmony search algorithm is then applied to solve such fractional models. Examples of both simulated and real data are provided. [ABSTRACT FROM PUBLISHER]

Mobile robots nowadays can assist wireless sensor networks (WSNs) in many jeopardizing scenarios that unexpectedly arise during their operational lifetime. We focus on an emerging kind of cooperative networking system in which a small team of robotic agents lies at a base station. Their mission is to service an already-deployed WSN by periodically replacing all damaged sensors in the field with passive, spare ones so as to preserve the existing network coverage. This novel application scenario is here baptized as “multiple-carrier coverage repair” (MC2R) and modeled as a new generalization of the vehicle routing problem. A hybrid metaheuristic algorithm is put forward to derive nearly-optimal sensor replacement trajectories for the robotic fleet in a short running time. The composite scheme relies on a swarm of artificial fireflies in which each individual follows the exploratory principles featured by Harmony Search. Infeasible candidate solutions are gradually driven into feasibility under the influence of a weak Pareto dominance relationship. A repair heuristic is finally applied to yield a full-blown solution. To the best of our knowledge, our scheme is the first one in literature that tackles MC2R instances. Empirical results indicate that promising solutions can be achieved in a limited time span. [ABSTRACT FROM PUBLISHER]

This paper provides a new approach to include the chemotactic behavior of Bacterial Foraging Algorithm (BFOA) in the existing Improved Harmony Search (IHS) algorithm. Extensive computer simulations with CEC-2005 benchmark functions reveal that the proposed algorithm outperforms the existing one with respect to accuracy in determining the optima. The proposed algorithm has successfully been implemented for multi-robot motion planning application. Performance has been studied using the proposed IHS-BFO algorithm and compared with existing IHS and Particle Swarm Optimization (PSO) algorithm. [ABSTRACT FROM PUBLISHER]

This paper investigates the efficiency of Harmony Search based algorithms for solving multi-objetive problems. For this task, four variants of the Harmony Search algorithm were adapted in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) framework. Harmony Search is a recent proposed music-inspired metaheuristic while NSGA-II is a very successful evolutionary multi-objective algorithm. The four proposed methods are tested against each other using a set of benchmark instances proposed in CEC 2009. The best proposed algorithm is also compared with NSGA-II. The preliminary results are very promising and stand the proposed approach as a candidate to the State-of-the-art for multi-objective optimization, encouraging further researches in the hybridization of the Harmony Search and Multi-objective Evolutionary Algorithms. [ABSTRACT FROM PUBLISHER]

Distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss, deviation of node voltages and service restoration while satisfying operating constraints. A Music Based Harmony Search algorithm (MBHS) is a recently developed algorithm which is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic search instead of a gradient search. In this paper, an Improved Music Based Harmony Search (IMBHS) algorithm is proposed to solve the network reconfiguration problem in order to minimize the power loss. Constant Complex Matrix Power Flow method is used to solve the radial distribution system. The proposed approach is tested on standard IEEE 33-bus system. The results are compared with MBHS algorithm and it is observed that the proposed method performed well in terms of the quality of solution. [ABSTRACT FROM PUBLISHER]

Harmony search (HS) is a recently proposed meta-heuristic by imitating music improvisation process, which has drawn much attention in the past few years. However, researches have revealed that the performance and the convergence rate of the method are suffered when dealing with high-dimensional or/and multimodal problems. To get a better control between exploitation and exploration, a hybrid HS algorithm is proposed, which is characterized in two aspects. First, the memory consideration scheme is modified by introducing crossover and mutation operators, which is inspired by the differential evolution (DE) algorithm. Second, two control parameters, namely PAR and bw, are either dynamically adjusted or self-learning along with the evolution process to fine-tune the solutions. Numerical results based on a test suite of well-known benchmark functions show that the proposed algorithm is more effective or at least competitive in finding near-optimal solutions compared with three HS variants and the DE/rand/1/bin algorithm. [ABSTRACT FROM PUBLISHER]

The basic Harmony Search (HS) algorithm has good global search performance, but it also has the disadvantage of slow local convergence speed especially when the iteration solution approaches to the optimal solution. Considering this disadvantage, the Accelerated Harmony Search (AHS) algorithm is proposed. The local convergence speed of the AHS algorithm is improved, using chaos disturbance strategy, parameter adaptive strategy and cluster analysis. The validity of the AHS algorithm is tested by five Benchmark functions. The results indicate that the AHS algorithm has better local convergence speed compared with those already proposed HS algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, Harmony Search is applied to the blocking job shop problem with makespan minimization. According to the characteristics of the considered problem, a decoding method is introduced to generate feasible solutions. A rule is proposed to improvise new harmonies. Some approaches are developed to determine the harmony search considering rate, the pitch adjusting rate, the dynamic harmony memory. A local search is investigated to further improve quality of the solutions. Results of numerical experiments on classical benchmark instances show that the proposed algorithm can improve makespan 16.50% on average. [ABSTRACT FROM PUBLISHER]

Harmony Search is a relatively new meta-heuristic algorithm for continuous optimization, in which its concept imitates the process of music improvisation. This paper applied an improved harmony search algorithm called Harmony Search with Adaptive Pitch Adjustment (HSAPA) for prediction of stock market index. HSAPA is applied to optimize the weights and biases of Single Multiplicative Neuron for the prediction of daily S&P500 index. Its prediction performance has been extensively evaluated using various sizes of dataset, training proportions, and beginning dates spanning from 1990 to 2009, a totaling of 108 test sets. The prediction results are compared to those of standard Back Propagation learning method and Opposition-based Differential Evolution algorithm, a very efficient and widely-accepted evolutionary algorithm. The results demonstrate that HSAPA is very promising for the stock market index prediction, measured with the mean absolute percentage error of the prediction results. [ABSTRACT FROM PUBLISHER]

This paper presents two modern approaches for solving economic dispatch (ED) problem considering piece wise quadratic cost function. Practical Economic dispatch problems have non-smooth fuel cost function with equality and inequality constraints which make it difficult to be effectively solved. In this paper modern heuristic methods such as shuffled frog leaping algorithm (SFLA) and harmony search (HS) are proposed for solving ED problem. The proposed approaches don't have the drawbacks of the classical heuristics such as local optimal trapping due to premature convergence, insufficient capability to find nearby extreme points, and lack of efficient mechanism to treat the constraints. The effectiveness and feasibility of the proposed methods were demonstrated on ten unit system with multiple fuel options. The experiments showed that the proposed approaches were capable of determining global solution. [ABSTRACT FROM PUBLISHER]

The present paper compares the application of one deterministic and three non-deterministic optimization algorithms to global transformer design optimization. One deterministic optimization algorithm (Mixed Integer Nonlinear Programming), is compared to three non-deterministic approaches (Harmony Search, Differential Evolution and Genetic Algorithm). The comparison yields significant conclusions on the efficiency of the algorithms and the selection of the most suitable for the transformer design optimization problem. [ABSTRACT FROM PUBLISHER]

The purpose of image steganalysis is to detect the presence of hidden messages in cover images. Steganalysis can be considered as a pattern recognition process to decide which class a test image belongs to: the cover photographic images or the stego-image. We present harmony search algorithm for feature selection for image steganalysis. Experiment show that the proposed hybrid algorithm for feature selection increases the testing accuracy of classifying result. The combination of the feature set extracted is likely to improve the performance of general steganalysis methods which have more real value for deterring covert communications and the uncorrelated features extracted contain more discriminatory information when distinguish different kinds of steganography. [ABSTRACT FROM PUBLISHER]

This paper studies logistics network design problem of fourth party logistics (4PL) in dynamic environment with disruptions to be considered. A two-stage stochastic optimization model is formulated. The first stage is to determine the network structure with the minimal total cost and the second stage is to minimize the operation cost of the network given disruption scenario. To solve it, a harmony search method is designed, which combines with Monte Carlo simulation. Several experiments are presented to test the significance of the model as well as the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

According to the shortages of simple genetic algorithm (GA) such as prematurity and low convergence speed, this paper firstly introduces the main process of harmony search algorithm (HS).Then based on analyzing the respective characteristic of GA and HS, introduce the HS into GA and a new hybrid algorithm is put forward. In this new algorithm, the harmony of HS and chromosome of GA are appropriately integrated as well as harmony memory and population. Furthermore, aiming at integrating two algorithms more dynamically, six dynamic hybrid genetic algorithms are put forward. The experimental results show the new algorithms are better than the simple genetic algorithm in solution quality, convergence speed and other indicators. It shows that the new dynamic hybrid algorithm are all feasible and effective. [ABSTRACT FROM PUBLISHER]

The production of a plant can be measured by various parameters like shoot length, number of leaves, root length, number of roots etc. During the growth of a plant, new leaves may appear and some leaves may fall. As a result, it is very difficult to predict the growth of the plant based on the leaf numbers. It is also difficult to measure the plant growth on the basis of root numbers as it grows underground. So, it is very convenient to measure the plant growth with respect to shoot length. In this paper, an effort has been made to predict the shoot length of a mustard plant by harmony search. The average error has been calculated based on the actual shoot length and predicted shoot length by harmony search. The result obtained in this paper has been compared with other soft computing and statistical methods which have been applied to the same problem. It has been observed that harmony search has given the minimum error compared to that of the others. The least square method has been applied on the predicted shoot length to find the shoot length at maturity. Finally, the pod yield has been predicted based on the shoot length at maturity. [ABSTRACT FROM PUBLISHER]

In this paper, a meta-heuristic approach called Adaptive Hybrid Harmony Search algorithm (AHHS), which is a variant of recently developed Harmony search optimisation algorithm is proposed. The harmony search is basically conceptualized using the musical process of searching for a perfect state of harmony. The proposed algorithm is hybridised with a customised neighbourhood search algorithm called ‘Variable depth neighbourhood search’, to improve its intensification mechanism. This proposed algorithm is applied to a combinatorial problem namely lay-up sequence optimisation of laminate composite stiffened cylinder. The results of the investigated numerical example, clearly illustrate the efficiency of the proposed algorithm to handle the design of layup sequence problem. [ABSTRACT FROM PUBLISHER]

Harmony search (HS) is a promising metaheuristic algorithm inspired by music improvisation process for various hard optimization problems. HS has attracted attention of researchers from different areas because it is easy to implement and can be applied to different optimization problems. in this paper, we present a novel method to improve the result of HS called Zipf harmony search (ZHS)â"by using the Zipf distribution to reference the harmonies within the harmony memory. We want to balance the intensification and diversification. Experimental results show that ZHS can provide better results than simple HS in the higher number of dimensions in numerical function optimization problem. [ABSTRACT FROM PUBLISHER]

This paper presents a novel clustering algorithm based on harmony Search (HS) with features of Artificial Bee Colony (ABC) and K-means algorithms. Harmony search (HS) is a stochastic meta-heuristic optimization algorithm inspired from the improvisation process of musicians. However, HS fails to concentrate on the crucial factor of mutating the harmony vectors with better values in the improvisation step (where they are just pitch adjusted). The food source exploitation feature of ABC algorithm is applied to improve the members of the Harmony Memory based on their fitness values and hence improves the convergence rate of the Harmony Search method. This concept in combination with K-Means clustering algorithm leads to a new algorithm. To claim the superiority of the novel algorithm, its performance has been compared with the traditional HS, PSO, and K-means. The simulation results show that the proposed algorithm outperforms the other algorithms in terms of accuracy and convergence speed. [ABSTRACT FROM PUBLISHER]

There are many problems related to pipe network rehabilitation, the main one being how to provide an increase in the hydraulic capacity of a system. Most of the Slovak irrigation pipe networks are branched, which is case elsewhere. A possible approach to increasing the hydraulic capacity of such systems is to convert them to looped networks (thereby providing alternative pathways) and increase their hydraulic capacity with minimum capital investment. In this work authors deal with application of simulation and optimization models for water management of existing sprinkler irrigation distribution systems, mainly in the case that rehabilitation of such systems is needed. A case study of the sprinkler irrigation system Tomasovo - Malinovo (south-west part of Slovakia) is presented in detail. [ABSTRACT FROM AUTHOR]

This paper presents the design and optimization procedure for slotless brushless DC (BLDC) motors. The motor optimization is achieved using a modified harmony search technique, though other optimization methods can also be used. The expressions of motor electromagnetic torque, speed, voltage, current, power losses, volume and cost are analytically represented as functions of motor geometric parameters. The objective function is a combination of motor power losses, volume and cost, and needs to be minimized subject to a set of electrical and mechanical requirements and limitations. Ten design parameters are considered including the number of poles, current density and the dimensions of magnet, stator, rotor, winding and air-gap. The effects of each design parameter on the brushless DC motor performance have been investigated. The method was originally conceived for the design of a small motor for a biomedical application, but in this paper one sample case of a larger motor illustrates the general use of the technique. [ABSTRACT FROM PUBLISHER]

In this paper we introduce the Largest Error First Harmony Search (LEFHS) algorithm for the deconvolution, or deblurring, of binary images. The algorithm requires no prior information about the source of the blur which is commonly modelled as a point spread function (PSF). LEFHS is able to fully recover the original binary image even when the PSF is unknown making it a true blind deconvolution algorithm. LEFHS addresses a major limitation that is present in another blind deconvolution algorithm called CHS. CHS is also able to fully recover the original binary image but only when the blur source is restricted to a binary PSF. LEFHS does not have this restriction and successfully recovers images blurred by arbitrary PSFs. LEFHS addresses a significant restriction in CHS as non-binary PSFs are the norm in real images. We compare our results with CHS and other state of the art binary deconvolution algorithms and find that LEFHS is faster than CHS even when using more complex non-binary PSFs as well as being more accurate than other binary deconvolution algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents an improved harmony search algorithms(IHS) based on genetic algorithm (GA) for solving the blocking flow shop scheduling problem. The largest position value (LPV) rule is used to convert continuous harmony vectors into job permutations and an efficient initialization scheme based on a variant of the NEH heuristic (NEH_PWT) is adopted to construct the initial harmony memory with certain quality and diversity. The computational result shows that the improved HS algorithm is not only better than HS algorithm, but also better than genetic algorithm and it is even better than the hybrid algorithms. [ABSTRACT FROM PUBLISHER]

Based on characteristics of Harmony Search and Simulated Annealing algorithm, a hybrid algorithm is designed to solve the multiprocessor task scheduling problem. Comparing with other typical optimization algorithm, the simulation results demonstrate that the hybrid algorithm is superior to Simulated Annealing and Harmony Search algorithm. It can improve the global search abilities and convergence speed and can escape local minimizer to look for better solutions. [ABSTRACT FROM PUBLISHER]

This paper proposes two Bio-inspired heuristic algorithms to solve the power distribution network reconfiguration problem for loss minimization, "real ants'-behavior-inspired" Ant Colony Optimization ACO implemented in the Hyper Cube HC framework and "musicians'-behavior-inspired" Harmony Search IIS algorithm. The optimization problem is formulated taking into account the operational constraints of distribution systems. A 32-bus and 118-bus distribution systems were selected for optimizing the configuration where both algorithms yield the optimum configuration with minimum power loss for each case study. However, the HS required shorter simulation time but more practice of the iterative process than the HC-ACO. Implementing the ACO in the HC framework resulted in a more robust and easier handling of pheromone trails than the standard ACO. [ABSTRACT FROM PUBLISHER]