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 new Hybrid Harmony Search (HHS) algorithm is introduced for discrete truss sizing problems.•Six test problems are solved.•The HHS algorithm is very competitive with literature.
The primary objective of CDEED (combined dynamic economic emission dispatch) problem is to determine the optimal power generation schedule for the online generating units over a time horizon ...considered and simultaneously minimizing the emission level and satisfying the generators and system constraints. The CDEED problem is bi-objective optimization problem, where generation cost and emission are considered as two competing objective functions. This bi-objective CDEED problem is represented as a single objective optimization problem by assigning different weights for each objective functions. The weights are varied in steps and for each variation one compromise solution are generated and finally fuzzy based selection method is used to select the best compromise solution from the set of compromise solutions obtained. In order to reflect the test systems considered as real power system model, the security constraints are also taken into account. Three new versions of DHS (differential harmony search) algorithms have been proposed to solve the CDEED problems. The feasibility of the proposed algorithms is demonstrated on IEEE-26 and IEEE-39 bus systems. The result obtained by the proposed CSADHS (chaotic self-adaptive differential harmony search) algorithm is found to be better than EP (evolutionary programming), DHS, and the other proposed algorithms in terms of solution quality, convergence speed and computation time.
•In this paper, three new algorithms CDHS, SADHS and CSADHS are proposed.•To solve DED with emission, poz's, spinning reserve and security constraints.•Results obtained by the proposed CSADHS algorithm are better than others.•The proposed CSADHS algorithm has fast convergence characteristic than others.
In this paper, we use a recently proposed algorithm—novel global harmony search (NGHS) algorithm to solve unconstrained problems. The NGHS algorithm includes two important operations: position ...updating and genetic mutation with a low probability. The former can enhance the convergence of the NGHS, and the latter can effectively prevent the NGHS from being trapped into the local optimum. Based on a large number of experiments, the NGHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and SGHS).
Display omitted
•An innovative and strong optimization technique based on harmony search is proposed.•The proposed algorithm is tested on solving economic emission dispatch problem.•It has the ...potential to be applied to many other engineering optimization problems.•Six test systems considering valve point effect and transmission losses are studied.•High quality solutions are obtained and compared with a large number of other methods.
This paper presents a new optimization technique developed based on harmony search algorithm (HSA), called chaotic improved harmony search algorithm (CIHSA). In the proposed algorithm, the original HSA is improved using several innovative modifications in the optimization procedure such as using chaotic patterns instead of uniform distribution to generate random numbers, dynamically tuning the algorithm parameters, and employing virtual harmony memories. Also, a novel type of local optimization is introduced and employed in the algorithm procedure. Applying these modifications to HSA has resulted in enhancing the robustness, accuracy and search efficiency of the algorithm, and significantly reducing the iterations number required to achieve the optimal solution. To validate the effectiveness of CIHSA, it is used to solve the combined economic emission dispatch (CEED) problem, which practically is a complex high-dimensional non-convex optimization task with several equality and inequality constraints. Six test systems having 6, 10, 13, 14, 40, and 140 generators are investigated in this study, and the valve-point loading effects, ramp rate limits and power transmission losses are also taken into account. The results obtained by CIHSA are compared with the results reported in a large number of other research works. Furthermore, the statistical data regarding the CIHSA performance in all test systems is presented. The numerical and statistical results confirm the high quality of the solutions found by CIHSA and its superiority compared to other existing techniques employed in solving CEED problems.
In this paper a new approach using Harmony Search (HS) algorithm is presented for placing Distributed Generators (DGs) in radial distribution systems. The approach is making use of a multiple ...objective planning framework, named an Improved Multi-objective HS (IMOHS), to evaluate the impact of DG placement and sizing for an optimal development of the distribution system. In this study, the optimum sizes and locations of DG units are found by considering the power losses and voltage profile as objective functions. The feasibility of the proposed technique is demonstrated in two distribution networks, where the qualitative comparisons are made against a well-known technique, known as Non-dominated Sorting Genetic Algorithm II (NSGA-II). Furthermore, the results obtained are compared with those available in the literature.
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.
Display omitted
•A novel hybrid HS-PABC algorithm has been proposed by integrating PSO embedded ABC algorithm (PABC) with Harmony search (HS) algorithm to enhance the HS algorithm ...performance.•Integrated approach of optimal network reconfiguration problem along with DG units and shunt capacitors compensation in radial distribution network using the proposed hybrid HS-PABC algorithm.•DG unit with real and reactive power injection capability is considered.•69 and 118 node RDN is used for validation.
This article presents the significance of efficient hybrid heuristic search algorithm(HS-PABC) based on Harmony search algorithm (HSA) and particle artificial bee colony algorithm (PABC) in the context of performance enhancement of distribution network through simultaneous network reconfiguration along with optimal allocation and sizing of distributed generators and shunt capacitors. The premature and slow convergence over multi model fitness landscape is the main limitation in standard HSA. In the proposed hybrid algorithm the harmony memory vector of HSA is intelligently enhanced through PABC algorithm during the optimization process to reach the optimal solution within the search space. In hybrid approach, the exploration ability of HSA and the exploitation ability of PABC algorithm are integrated to blend the potency of both algorithms. The box plot and Wilcoxon rank sum tests are used to show the quality of the solution obtained by hybrid HS-PABC with respect to HSA.The computational results prove the integrated approach of the network reconfiguration problem along with optimal placement and sizing of DG units and shunt capacitors as an efficient approach with respect to power loss reduction and voltage profile enhancement. The results obtained on 69 and 118 node network by hybrid HS-PABC method and the standard HSA reveals the effeciency of the proposed approach which guarantees to achieve global optimal solution with less iteration.
Display omitted
•A novel and hybrid optimization technique called HSCOA for optimum tuning of fuzzy PID controller for LFC of interconnected power systems is developed.•Sensitivity analysis is ...carried out to verify the robustness of HSCOA based fuzzy PID controller.•The effect of nonlinearity by considering GDB in the transient responses of the concerned power plant is investigated.•The effectiveness of designed controllers is evaluated under different scenarios of loading conditions.
In this paper, a hybrid algorithm is developed by incorporating the egg-laying and immigration mechanisms of cuckoo optimization algorithm (COA) into harmony search (HS) algorithm (HSCOA), to design a secondary controller for two practical models of load frequency control (LFC) problem. Initially, a two-area non-reheat thermal power system is considered and the gains of PID and fuzzy PI/PID controllers are adjusted by the proposed tuning method. The superiority of HSCOA in regulating controller gains is demonstrated by evaluation and comparison of the obtained transient outcomes over some other published approaches in literature. To prove the satisfaction of the robustness in designed LFC by means of the proposed method, the performance of HSCOA based fuzzy PID controller is extensively verified under varying loading condition and some critical parameters related to the considered power plant. To add further practical challenge, the governor dead band (GDB) is included in the concerned system modeling to study the advantages of the HSCOA tuned fuzzy PID controller in handling the properties of nonlinearity in the system model. Time domain simulation of transient responses indicates that the designed controller operates satisfactorily to deal with the GDB nonlinearity and outperform other published techniques. Furthermore, to demonstrate the effective feasibility of the proposed method, the study is extended to a two-area multi-source power system with/without consideration of HVDC link. It is observed that HSCOA optimized fuzzy PID controller gives superior quality outcomes in comparison to other reported strategies. Finally, the robustness of the controller’s gains designed for the concerned power system is investigated under various scenarios of change in size, location and pattern of step load perturbation.
•Integrating metaheuristics and ANN for improved stock price prediction.•Both topology of ANN and the number of inputs are optimized.•The number of the input variables is reduced to almost its ...half.•HS-ANN has better generalization ability than GA-ANN model.•Proposed methodologies outperformed both in statistical and financial terms.
Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting.
PDF
