Pitch Adjustment Research Articles

Modified Parameters of Harmony Search Algorithm for Better Searching

The scheduling and rostering problems are deliberated as integrated due to they depend on each other whereby the input of rostering problems is a scheduling problems. In this research, the integrated scheduling and rostering bus driver problems are defined as maximising the balance of the assignment of tasks in term of distribution of shifts and routes. It is essential to achieve is fairer among driver because this can bring to increase in driver levels of satisfaction. The latest approaches still unable to address the fairness problem that has emerged, thus this research proposes a strategy to adopt an amendment of a harmony search algorithm in order to address the fairness issue and thus the level of fairness will be escalate. The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration. These parameters influence the overall performance of the HS algorithm, and therefore it is crucial to fine-tune them. The contributions to this research are the HMCR parameter using step function while the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter. The model of constant step function is introduced in the alteration of HMCR parameter. The experimental results revealed that our proposed approach is superior than parameter adaptive harmony search algorithm. In conclusion, this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers, which contributed to the lowering of illness, incidents, absenteeism and accidents.

Stability improvement of PV‐BESS diesel generator‐based microgrid with a new modified harmony search‐based hybrid firefly algorithm

Using a new improved harmony search-based hybrid firefly algorithm (IHBFA), a comprehensive controller gain parameter estimation of all distributed resources-based microgrid is proposed. To ensure a fast convergence and to endeavour less randomisation to conventional firefly algorithm (FA), diversity of population is increased by an improved harmony search (HS) algorithm. To decrease local optima searching delay, a linear incremental pitch adjustment rate and exponential decaying bandwidth is considered for proposed HS-based hybrid FA. Photovoltaic (PV), an auxiliary battery energy storage system (BESS) with the second-order phase-locked loop control, is considered as a primary DG ( DG 1) for the proposed microgrid. Pade approximation delay-based governor control is used for the diesel generator unit, considered as a secondary DG ( DG 2). The overall gain optimisation improves the dynamic stability limits by minimising low-frequency network behaviour. The effectiveness of proposed IHBFA in terms of power oscillation damping and improved stability limits is clearly demonstrated for microgrid applications.

Distribution generation by photovoltaic and diesel generator systems: Energy management and size optimization by a new approach for a stand-alone application

Hybrid energy systems have attracted significant attention to supply the power requirements of stand-alone areas. Among different configurations, photovoltaic-diesel (PV/diesel) power generation systems are one of the most promising ones for their good performance. Owing to the complexity of the hybrid PV/diesel system, optimal balance between these two sources needs particular attention to find a good engineering solution. This paper focuses on optimal sizing of a PV/diesel power generation system by harmony search (HS) algorithm. Three new pitch adjustment mechanisms are introduced to enhance the diversification and intensification capabilities of HS. The results are compared with the results obtained by original HS, particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results show that HS-II which focuses on intensification, finds more promising results than the other investigated methods. Moreover, hybridization of PV and diesel generator reduces the system costs and pollutant emissions.

Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines

To maximize power extraction at below-rated wind speeds, variable-speed wind turbines must be controlled by tracking the optimal TSR (tip speed ratio) and pitch angle, which depend on the wind speed measured by nacelle anemometers or provided by an EWS (effective wind speed) estimator. However, the measured values are imprecise and existing estimators cannot provide qualified estimates. This paper addresses this problem by presenting a novel solution with a non-standard extended Kalman filter. To avoid using imprecise wind speed measurements or other costly measurement devices, the proposed solution employs a virtual measurement that is calculated from related estimated states. In addition, the solution presents an internal EWS model by considering the tower shadow effect, so the obtained model is more general than the statistical model that is difficult to obtain in practice. Compared with existing estimators, the proposed estimator provides more precise estimated results and is suitable for control application. Its application is investigated on the MPE (maximum power extraction) of a variable speed wind turbine, for which an industrial baseline controller is optimized by enhancing the optimal TSR tracking and pitch adjustment. The proposed solutions are validated using both simulation and field testing results. Comparing the proposed estimation solution to two existing methods demonstrates that the former gives the best estimate results. Moreover, its application for the MPE increases annual energy production by approximately 0.8% in comparison with the baseline controller, which is a considerable energy production increment.

An improved harmony search-based functional link higher order ANN for nonlinear data classification

To obtain the optimal set of weights in any higher order artificial neural network, it is often laborious to adjust the set of weights by using appropriate learning algorithm. In this paper, an improved variant of harmony search (HS), called improved harmony search (IHS) along with gradient descent learning (GDL) is used with functional link artificial neural network (FLANN) for the task of classification in data mining. IHS performs better than HS by eliminating constant parameters [bandwidth (bw), pitch adjustment rate (PAR)] in HS algorithm and incorporating changes dynamically in PAR and bw with iteration. The searching capability of IHS to obtain optimal harmony is used along with GDL to discover optimal set of weights for FLANN model. The proposed IHS-GDL-FLANN is implemented in MATLAB and compared with other alternatives. In order to get statistical correctness of results, the proposed method is analysed by using various statistical analysis under null-hypothesis.

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.

An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem

This paper presents an improved fruit fly optimization algorithm (IFFOA) for solving the multidimensional knapsack problem (MKP). In IFFOA, the parallel search is employed to balance exploitation and exploration. To make full use of swarm intelligence, a modified harmony search algorithm (MHS) is proposed and applied to add cooperation among swarms in IFFOA. In MHS, novel pitch adjustment scheme and random selection rule are developed by considering specific characters of MKP and FOA. Moreover, a vertical crossover is designed to guide stagnant dimensions out of local optima and further improve the performance. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify that the proposed algorithm is an effective alternative for solving the MKP.

Seismic Reliability–Based Multiobjective Design of Water Distribution System: Sensitivity Analysis

AbstractThis study proposes a seismic reliability–based water distribution system (WDS) optimal design model that minimizes total cost and maximizes seismic reliability. Here, seismic reliability is defined as the ratio of the available quantity of water to the required demand under stochastic earthquake events. A new evaluation model is used to assess seismic reliability, while a multiobjective harmony search (MOHS) based on a ranking approach is used for optimization. The Anytown network was modified for the demonstration of the proposed method. First, this study performs the sensitivity analysis of MOHS parameter values [i.e., harmony search consideration rate (HMCR) and pitch adjustment rate (PAR)] to identify the best parameter set in a pipe-sizing problem of an Anytown network. Then, Pareto optimal solutions with three different tank configurations are obtained and compared with respect to the final Pareto fronts and the system designs. For the sensitivity analysis, it reveals that higher PAR and lo...

A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation

In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate) parameters that improve the convergence rate of traditional harmony search algorithm (HS). The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.

Harmony search based remodularization for object-oriented software systems

Software remodularization is always a key task in the field of software reengineering. In recent years, search-based optimization techniques have been considered as an effective method to handle software remodularization problems. Recently, Harmony Search (HS), a metaheuristic algorithm has gained wide attention and has been demonstrated to be effective and convenient to solve various science and engineering problems. The applicability and usefulness of HS algorithm has not been studied by any researcher till date to solve the software remodularization problem. This paper proposes a Harmony Search-Based Remodularization Algorithm (HSBRA) to solve the software remodularization problem for object-oriented software (OOS) systems. To do so, several key improvements have been put forward like an efficient encoding of harmony memory, initialization of harmony memory, an effective strategy for improvisation of a new harmony. In addition, a new fitness function that considers coupling, cohesion, package count index and package size index is developed. Four different variants of HSBRA (i.e., HSBRA1, HSBRA2, HSBRA3, and HSBRA4) based on linear and exponential changes in Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR) have been formulated. The proposed approach is tested over 8 problem instances and results are compared with both the population based (Genetic Algorithm - GA, Differential Evolution – DE, and Artificial Bee Colony - ABC) and single-solution based (Simulated Annealing - SA and Hill-Climbing - HC) algorithms. A Wilcoxon test is performed to assess the pair wise statistical performance of the algorithms. The results show that HSBRA outperforms SA, HC, and GA algorithms and performs better than ABC algorithms. Out of four variants of HSBRA, exponential change based variants of HSBRA perform better than linear change based variants.

Hollow Blades for Small Wind Turbines Operating at High Altitudes

Since the air density reduces as altitude increases, operation of small wind turbines (SWTs), which usually have no pitch adjustment, remains challenging at high altitudes due largely to the reduction of starting aerodynamic torque. By reducing the moment of inertia through the use of hollow blades, this study aims to speed up the starting while maintaining the structural integrity of the blades and high output power. A horizontal axis turbine with hollow blades was designed for two sites in Iran with altitude of 500 m and 3000 m. The design variables are the distributions of the chord, twist, and shell thickness and the improvement of output power and starting are the design goals. Blade-element momentum (BEM) theory was employed to calculate these goals and beam theory was used for the structural analysis to investigate whether the hollow timber blades could withstand the aerodynamic and centrifugal forces. A combination of the goals formed the objective function and a genetic algorithm (GA) was used to find a blade whose output power at a predetermined tip speed ratio (TSR) and the starting performance were high while the stress limit was met. The results show that hollow blades have starting times shorter than solid ones by approximately 70%. However, in the presence of generator resistive torque, the algorithm could not find a blade for an altitude of 3000 m. To solve that problem, the tip speed ratio was added to other design variables and another optimization was done which led to the optimal blades for both altitudes.

Proportional–integral–derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm

Output powers of wind turbines (WTs) with variable blade pitch over nominal wind speeds are controlled by means of blade pitch adjustment. While tuning the blade pitch, conventional proportional–integral–derivative (PID) controllers and some intelligent genetic algorithms (IGAs) are widely used in hot systems. Since IGAs are community-based optimisation methods, they have an ability to look for multi-point solutions. However, the PID parameter setting optimisation of the IGA controllers is important and quite difficult a step in WTs. To solve this problem, while the optimisation is carried out by regulating mutation rates in some IGA controllers, the optimisation is conducted by altering crossover point numbers in others. In this study, a new IGA algorithm approach has been suggested for the PID parameter setting optimisation of the blade pitch controller. The algorithm rearranging both the mutation rate and the crossover point number together according to the algorithm progress has been firstly used. The new IGA approach has also been tested and validated by using MATLAB/Simulink software. Then, its superiority has been proved by comparing the other genetic algorithm (GAs). Consequently, the new IGA approach has more successfully adjusted the blade pitch of a WT running at higher wind speeds than other GA methods.

A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure

This paper presents a new variant of the Harmony Search (HS) algorithm. This Hybrid Harmony Search (HHS) algorithm follows a new approach to improvisation: while retaining HS algorithm Harmony Memory and pitch adjustment functions, it replaces the HS algorithm randomization function with Global-best Particle Swarm Optimization (PSO) search and neighbourhood search. HHS algorithm performance is tested on six discrete truss structure optimization problems under multiple loading conditions. Optimization results demonstrate the excellent performance of the HHS algorithm in terms of both optimum solution and the convergence behaviour in comparison with various alternative optimization methods.

A differential-based harmony search algorithm for the optimization of continuous problems

The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings and the initialization of the Harmony Memory (HM). To address these issues, this paper presents a new variant of the HS algorithm, which is called the DH/best algorithm, for the optimization of globally continuous problems. The proposed DH/best algorithm introduces a new improvisation method that differs from the conventional HS in two respects. First, the random initialization of the HM is replaced with a new method that effectively initializes the harmonies and reduces randomness. Second, the conventional pitch adjustment method is replaced by a new pitch adjustment method that is inspired by a Differential Evolution (DE) mutation strategy known as DE/best/1. Two sets of experiments are performed to evaluate the proposed algorithm. In the first experiment, the DH/best algorithm is compared with other variants of HS based on 12 optimization functions. In the second experiment, the complete CEC2014 problem set is used to compare the performance of the DH/best algorithm with six well-known optimization algorithms from different families. The experimental results demonstrate the superiority of the proposed algorithm in convergence, precision, and robustness.

Stacking sequence optimization for maximum buckling load of composite plates using harmony search algorithm

This paper presents the application of the harmony search algorithm (HSA) and some of its variants in the optimization of the stacking sequence of laminated composites. The optimization objective is to maximize the bucking load of a symmetric and balanced laminated plate. Performance and sensitivity to the parameters harmony memory size (HMS), harmony memory consideration rate (HMCR) and pitch adjusting rate (PAR) are determined based on the algorithm apparent reliability. A modified version of the pitch adjustment is proposed and its positive impact on the reliability is demonstrated. The best performance of the basic HSA is obtained using low PAR with high HMS and HMCR. The variants of HSA consider a linear variation of the pitch adjusting rate during the optimization. Increasing PAR reduce the HSA sensitivity to this parameter and lead to fast rise of reliability in the beginning of the optimization and high reliability at the end of the search. Decreasing PAR resulted in excellent reliability for long searches, but the chance to find a global optimum is drastically reduced for short optimizations. The last example demonstrate that HSA is more efficient than other metaheuristic methods, although it is less reliable than a special version of genetic algorithm.

A modified harmony search method for biclustering microarray gene expression data

Microarray experiments generate vast amounts of data, leading to new requirements and challenges for bioinformatics. The crucial step in considering gene expression data is to discover a group of genes that have similar patterns. Biclustering is a two-dimensional clustering problem where the genes and samples are grouped simultaneously. Latest research showed that biclustering has a great potential in finding marker genes that are associated with certain tissues or diseases. Here, we present an efficient biclustering algorithm called Modified Harmony Search (MHS) method. The MHS algorithm includes a step for generating the new solution by using levy flight. Levy flight increases the diversity of the solutions of harmony search (HS) algorithm and the Pitch Adjusting Rate (PAR) is changed dynamically to escape from local optima. To know the performance of the MHS, it is applied on various benchmark optimisation functions and the benchmark gene expression data sets. The experimental results show that the MHS outperforms HS in optimisation functions and biclustering problems. Also, this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.

バイオリン演奏ロボットの音程制御に関する基礎的研究

バイオリン演奏ロボットの音程制御に関する基礎的研究

Fuzzy harmony search algorithm based optimal power flow for power system security enhancement

Abstract This paper proposes the integration of fuzzy logic system with harmony search algorithm (FHSA) to find the optimal solution for optimal power flow (OPF) problem in a power system. The objective of the method is to minimize the total fuel cost of thermal generating units having quadratic cost characteristics and severity index (SI). The generator active power, generator bus voltage magnitude, transformer taps, VAR of shunts and the reactance of thyristor controlled series capacitor (TCSC) are taken as the control variables. The adjustment of proposed algorithm parameters such as pitch adjustment rate (PAR) and bandwidth (BW) is done through fuzzy logic system (FLS). The effectiveness of the proposed method has been tested on the standard IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems in MATLAB environment and their results are compared with conventional harmony search algorithm (HSA) and other heuristic methods reported in the literature recently.

A self adaptive harmony search based functional link higher order ANN for non-linear data classification

In the data classification process involving higher order ANNs, it’s a herculean task to determine the optimal ANN classification model due to non-linear nature of real world datasets. To add to the woe, it is tedious to adjust the set of weights of ANNs by using appropriate learning algorithm to obtain better classification accuracy. In this paper, an improved variant of harmony search (HS), called self-adaptive harmony search (SAHS) along with gradient descent learning is used with functional link artificial neural network (FLANN) for the task of classification in data mining. Using its past experiences, SAHS adjusts the harmonies according to the maximum and minimum values in the current harmony memory. The powerful combination of this unique strategy of SAHS and searching capabilities of gradient descent search is used to obtain optimal set of weights for FLANN. The proposed method (SAHS–FLANN) is implemented in MATLAB and the results are contrasted with other alternatives (FLANN, GA based FLANN, PSO based FLANN, HS based FLANN, improved HS based FLANN and TLBO based FLANN). To illustrate its effectiveness, SAHS–FLANN is tested on various benchmark datasets from UCI machine learning repository by using 5-fold cross validation technique. Under the null-hypothesis, the proposed method is analyzed by using various statistical tests for statistical correctness of results. The performance of the SAHS–FLANN is found to be better and statistically significant in comparison with other alternatives. The SAHS–FLANN differs from HS–FLANN (HS based FLANN) by the elimination of constant parameters (bandwidth and pitch adjustment rate). Furthermore, it leads to the simplification of steps for the improvisation of weight-sets in IHS–FLANN (improved HS based FLANN) by incorporating adjustments of new weight-sets according to the weight-sets with maximum and minimum fitness.

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.