Improved Harmony Search Algorithm Research Articles

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

Development and Sensitivity Analysis of an Improved Harmony Search Algorithm with a Multiple Memory Structure for Large-Scale Optimization Problems in Water Distribution Networks

The continuous supply of drinking water for human life is essential to ensure the sustainability of cities, society, and the environment. At a time when water scarcity is worsening due to climate change, the construction of an optimized water supply infrastructure is necessary. In this study, an improved version of the Harmony Search Algorithm (HSA), named the Maisonette-type Harmony Search Algorithm (MTHSA), was developed. Unlike the HSA, the MTHSA has a two-floor structure, which increases the optimizing efficiency by employing multiple explorations on the first floor and additional exploitations of excellent solutions. Parallel explorations enhance the ability in terms of exploration (global search), which is the tendency to uniformly explore the entire search space. Additional exploitations among excellent solutions also enhance the ability of local searches (effective exploitation), which is the intensive exploration of solutions that seem to have high possibilities. Following the development of the improved algorithm, it was applied to water distribution networks in order to verify its efficiency, and the numerical results were analyzed. Through the considered applications, the improved algorithm is shown to be highly efficient when applied to large-scale optimization problems with large numbers of decision variables, as shown in comparison with the considered optimizers.

Providing an Intelligent Frequency Control Method in a Microgrid Network in the Presence of Electric Vehicles

Due to the reduction in fossil fuel abundance and the harmful environmental effects of burning them, the renewable resource potentials of microgrid (MG) structures have become highly highly. However, the uncertainty and variability of MGs leads to system frequency deviations in islanded or stand-alone mode. Usually, battery energy storage systems (BESSs) reduce this frequency deviation, despite limitations such as reducing efficiency in the long term and increasing expenses. A suitable solution is to use electric vehicles (EVs) besides BESSs in systems with different energy sources in the microgrid structure. In this field, due to the fast charging and discharging of EVs and the fluctuating character of renewable energy sources, controllers based on the traditional model cannot ensure the stability of MGs. For this purpose, in this research, an ultra-local model (ULM) controller with an extended state observer (ESO) for load frequency control (LFC) of a multi-microgrid (MMG) has been systematically developed. Specifically, a compensating controller based on the single-input interval type fuzzy logic controller (FLC) was used to remove the ESO error and improve the LFC performance. Since the performance of the ULM controller based on SIT2-FLC depends on specific parameters, all of these coefficients were adjusted by an improved harmony search algorithm (IHSA). Simulation and statistical analysis results show that the proposed controller performs well in reducing the frequency fluctuations and power of the system load line and offers a higher level of resistance than conventional controllers in different MG scenarios.

An Adaptive Harmony Search Part-of-Speech tagger for Square Hmong Corpus

Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns.

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

Optimal configuration for the wind-solar complementary energy storage capacity based on improved harmony search algorithm

With the increase in the permeability of renewable energy, the randomness and uncertainty of photovoltaic power generation and wind power generation have an impact on the stable operation of the power grid. Energy storage technology can effectively improve the controllability of new energy, but the optimal allocation of energy storage capacity has always been a difficult problem limiting the development of energy storage technology. In this paper, the capacity optimization model of the complementary energy storage system is established based on the analysis of the wind-solar energy storage principle and the energy balance criterion. Then, with the minimum investment cost, the highest economic benefit, low wind and light abandonment rate and low load power shortage rate as the optimization objectives, the harmony search algorithm was used to solve the optimization model under the constraint of stable operation of the system, and the optimal capacity allocation scheme was obtained. The results of the example show that the optimization strategy in this paper can reduce the investment cost of the system and improve the reliability of the power supply and energy efficiency of the system.

An improved harmony search algorithm using opposition-based learning and local search for solving the maximal covering location problem

In this article, an improved harmony search algorithm (IHSA) that utilizes opposition-based learning is presented for solving the maximal covering location problem (MCLP). The MCLP is a well-known facility location problem where a fixed number of facilities are opened at a given potential set of facility locations such that the sum of the demands of customers covered by the open facilities is maximized. Here, the performance of the harmony search algorithm (HSA) is improved by incorporating opposition-based learning that utilizes opposite, quasi-opposite and quasi-reflected numbers. Moreover, a local search heuristic is used to improve the performance of the HSA further. The proposed IHSA is employed to solve 83 real-world MCLP instances. The performance of the IHSA is compared with a Lagrangean/surrogate relaxation-based heuristic, a customized genetic algorithm with local refinement, and an improved chemical reaction optimization-based algorithm. The proposed IHSA is found to perform well in solving the MCLP instances.

Improved Harmony Search Algorithm for Enhancing Efficiency and Quality in Optimization of the Distillation Process.

Reducing production costs is one of the main objectives of process intensification; in this work, production costs of the distillation process are reduced by reducing equipment size and utility consumption from the perspective of process optimization to achieve the purpose of process intensification. The application of intelligent optimization algorithms in the optimization process of distillation is vital to achieving high efficiency and low costs. Combining the harmony search algorithm with the characteristics of distillation optimization, a new distillation harmony search algorithm (DHSA) was proposed, which includes the self-adaptive adjustment of parameters, roulette selection strategy, and ratio optimization strategy. Benefiting from the DHSA, the optimal total annual cost and calculation times were remarkably reduced when compared with reported algorithms in the optimization of four distillation cases including the two-column model, three-column model, reactive distillation column model, and dividing-wall extractive distillation column model. In addition, the highest coefficient of variation of DHSA in 10 parallel calculations is 1.25%. These results indicate that DHSA has the advantages of a higher-quality solution, less computing time, and higher stability, which not only improves the optimization efficiency and quality but also inspires the optimization strategies for other algorithms.

Voltage profile improvement and power loss minimization of new haven injection substation for optimal placement of DG using harmony search algorithm optimization technique

The main importance of a power grid is to transfer electrical energy from the production to the consumption, while maintaining an acceptable reliability and voltage quality for all customers. The role of electrical power distributions to have centralized plants distributing electricity through Distributed Generation (DG) which reduces the Power Loss on transmission and distribution station and also improve voltage profile by optimization. This research work analyzes New Haven injection substation for optimal placement of DG generation based on wind power renewable energy source to the Distribution network and voltage stability using Harmony Search Algorithm. The model developed was simulated using MATLAB/SIMULINK software. The work applied new improved Harmony Search Algorithm for integration of renewable energy generation in the distribution network with positive impact on the operation of the voltage stability between 0.95 to 1.05p.u and reactive power distribution improvement through minimization of line losses. The total power system Loss without and with DG are 338.712KW and 307.9KW. Also, the voltage profiles of the sixteen faulty buses are stabilized for efficient performance of substation. Hence, the percentage of power system improvement is 27.23%. Therefore from the results, there is reduction of power Loss when DG is applied and optimised in the power system.

Joint economic and emission load dispatch for the Algerian grid via an improved harmony search algorithm

Joint economic and emission load dispatch for the Algerian grid via an improved harmony search algorithm

Demand side management and size optimization for an integrated renewable energy system considering deferrable loads

AbstractWith the emergence of various technologies in renewable energy sources (RESs), consumers nowadays have led to multiple techniques for integrating various resources. An optimum energy flow management delivers an effective, reliable integrated renewable energy system (IRES) with higher efficiency. In this paper, an optimum model for the IRES is designed through the optimal sizing of system components for an estimated demand. Using integer linear programming, an energy management scheme has been developed based upon scheduling of energy utilization technique of deferrable loads. The considered approach is modeled to reduce the peak energy utilization (hourly) of the area of study through the consumption scheduling of machines. Furthermore, the MHP‐solar‐wind‐battery‐based IRES with energy Management strategy has been optimized in MATLAB. Size optimization of the system has been done without and with the energy management strategy using improved harmony search algorithm. The results show a significant improvement in the cost and savings using the strategy. Considering the 5% LPSP, the optimum size for the integrated system offers a total annualized cost of INR 3.21 million with a levelized cost of energy of 4.5 INR/kWh. The comparison of proposed method with PSO shows the better convergence rate of the system. Comparing systems with and without energy management, it has been noted that with a strategy, systems size and expenditures are reduced significantly.

Speed Control of DC Series Motor PV System via Improved Harmony Search Algorithm

This article introduces the speed adjustment of the DC motor solar system. The developed design matter of the speed controller is established as an optimization problem. Improved Harmony Search Algorithm (IHSA) is employed to seek for optimum Proportional Integral (PI) parameters of speed controller by diminishing the time domain cost function. The performance of the suggested IHSA based speed control of DC motor has been contrasted with Particle Swarm Optimization (PSO) and the traditional PI controller found by Ziegler Nichols (ZN) under diverse operating cases and disturbances. The results of the suggested IHSA are confirmed via time domain analysis, and diverse performance indices. Simulation results have proven the influence of the developed approach in adjusting the speed of the DC motor over PSO and traditional algorithms.

Improved Harmony Search Algorithm for Speed Control of DC Series Motor PV System

This article introduces the speed control of the DC motor solar system. The developed design matter of the speed controller is established as an optimization problem. Improved Harmony Search Algorithm (IHSA) is employed to seek for optimum Proportional Integral (PI) parameters of speed controller by diminishing the time domain cost function. The performance of the suggested IHSA based speed control of DC motor has been contrasted with Particle Swarm Optimization (PSO) and the traditional PI controller found by Ziegler Nichols (ZN) under diverse operating cases and disturbances. The results of the suggested IHSA are confirmed via time domain analysis, and diverse performance indices. Simulation results have proven the influence of the developed approach in adjusting the speed of the DC motor over PSO and traditional algorithms.

Improved Harmony Search Algorithm for Multihop Routing in Wireless Sensor Networks

Improved Harmony Search Algorithm for Multihop Routing in Wireless Sensor Networks

Improved Harmony Search Algorithm to Compute the Underfrequency Load Shedding Parameters

The power system continuously deals with frequency fluctuations. When a power disturbance occurs, the transmission system operators rely on the underfrequency load shedding (UFLS) scheme to address severe underfrequency (UF) events to maintain the frequency at the permissible level and prevent blackouts. Defining settings of a conventional UFLS scheme is a very complex problem due to the nonlinear nature of the frequency response, and the size of the problem is vast because of the number of UF-relays spread on the power system. The under or over total load disconnection produced by the wrong setting of the UF-relays can create secondary frequency events or even a total blackout. This paper introduces a novel method to compute an optimally parametrized conventional UFLS scheme in specific given operating conditions by formulating it as a constrained problem and using the Improved Harmony Search (IHS) algorithm to solve it. Since there is no previous knowledge of using IHS to solve the UFLS scheme, a numerical parameter sensitivity analysis (PSA) is developed to tune the parameter of the IHS algorithm. The IEEE 39-bus system was modelled in DIgSILENT® PowerFactory™ and used as a test system. The optimally parametrized conventional UFLS methodology presented in this paper reveals superior results against the conventional UFLS scheme, and the suitability of using the IHS algorithm is confirmed.

Random-sampling-based multi-UAV cooperative search planning for high-rise firefighting

This paper proposes a multi-UAV cooperative search planning method based on random sampling for high-rise fire rescue. First, an optimal distribution method of search points inspired by the random tree sampling search algorithm is proposed, which uses its repeated search feature in a limited complex space to solve the problems of waypoint distribution and task assignment for multiple UAVs simultaneously. Second, a waypoint optimization method based on transaction rules is designed to further improve search coverage. Third, the optimal traversal order of waypoints for each UAV is obtained by using the improved harmony search algorithm to acquire the planned path of each UAV. Finally, the multithreading method is used to simulate the distributed operation in the actual task to verify the feasibility of the proposed method.

Seismic analysis and optimization of concrete bridge under the moving train utilizing numerical methods and adaptive improved harmony search algorithm

Abstract In this paper, the simultaneous actions of moving force and seismic load on the dynamic displacement and optimization of the concrete bridge are studied. The sinusoidal shear deformation beam theory is employed for the modelling of the concrete bridge mathematically. The structural damping of the concrete bridge is assumed by the Kelvin–Voigt theory. Utilizing the method of energy and Hamilton’s law, the equations of motion are obtained. Three mixed numerical methods, including the integral quadrature, harmonic differential quadrature method, and Newmark technique, are presented for the numerical outcomes of the differential equations. Utilizing adaptive improved harmony search, improved harmony search, harmony search, and global harmony search algorithms, the optimization process of the concrete bridge is examined. The mentioned algorithm is improved adaptively by utilizing dynamic deflection. The harmony memory is corrected at first and second adjustments, respectively, based on emotional bandwidth and step size randomly. The optimum conditions of the concrete bridge are evaluated with various harmony existing search methods. The role of multiple parameters, including the velocity and acceleration of moving load, length and thickness of bridge, boundary conditions, and the amplitude of carrying load, in the dynamic displacement of the bridge is studied. The numerical results indicate that with increasing the velocity and acceleration of the moving train, the dynamic displacement of the concrete bridge increases. In addition, with increasing the length of the bridge, the time of maximum deflection (i.e. when the train is in the middle of the bridge) is increased. It is concluded for the concrete bridge under the seismic load that the optimum values of the bridge’s length and thickness are decreased (about 24%) and increased (about 21%), respectively. The optimum values of amplitude, velocity, and acceleration of moving train are decreased, respectively, about 34%, 33%, and 29% in the case of the concrete bridge under the earthquake load. In addition, the optimum length of the concrete bridge is decreased significantly, with increasing the moving load amplitude, velocity, and acceleration.

Comparison of Passive and Active Tuned Mass Dampers for Structures Subjected to Earthquake Excitations

Passive and active control systems have been used in practical applications for new or existing structures. Active systems have generally better performance compared to passive ones, but the control force ad time-delay effects in the production of this control force are needed to be considered. In this study, passive tuned mass dampers (TMDs) and active tuned mass dampers (ATMDs) are compared for structures subjected to earthquake motions. For optimization of both systems, an improved harmony search algorithm is proposed. In the optimum design of ATMD, time-delay and control force limitation are considered. The comparison is presented for a 15-story reinforced concrete (RC) frame structure. According to the results, ATMD is effective than TMD in the reduction of structural displacement.

A novel approach for optimal synthesis of path generator four-bar planar mechanism using improved harmony search algorithm

ABSTRACT Since last few decades, a lot of work has been done on the evolutionary techniques to solve the optimisation problems. With the time passes, these techniques were modified, and also new algorithms were introduced to improve the performance. These techniques had been used by many researchers in synthesis of mechanisms to get the optimum results with minimum design errors. Improved harmony search (IHS) algorithm is developed from harmony search technique by improvising the harmony in HS algorithm. This technique is inspired by searching the best state of harmony in musical process in which a jazz musician make practice after practice to find the same. In this paper, IHS algorithm is utilized to synthesize four-bar path generation mechanism. A mathematical model was derived using vector loop closure equation, wherein the error function, representing positional error between actual and desired points, was considered for forming objective function. The penalty function was used to prevent violation of structural constraints corresponding to Grashof’s criteria and input crank angle sequence. Three different cases were studied with ten, fifteen and eighteen precision points with and without prescribed timings. The results obtained from the study, compared with other evolutionary techniques from literature, and found significant competition.

The Application of Improved Harmony Search Algorithm to Multi-UAV Task Assignment

In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints.