Harmony Search Research Articles

ReUse: Reliable and efficient RSU-enabled relay vehicle selection using harmony search and EDAS for message dissemination in VANETs

Vehicular ad hoc networks (VANETs) are expected to provide the driver and passengers of a vehicle a number of safety and non-safety services in the very near future. The safety and non-safety related information are forwarded by the vehicles in the network so that the information reaches the intended vehicles. The vehicles can use either the broadcast links for forwarding the information or via roadside units (RSUs). As the safety (emergency) information intended for vehicles must be sent over the network in real time, the vehicles in the network must use a broadcast method for communication. However, the broadcast storm problem in the network leads to improper utilization of network resources. In this work, to alleviate the consequences of the broadcast storm, we propose an efficient relay vehicle selection mechanism ReUse for selecting the promising relay vehicle(s) in the network for data dissemination. The relay vehicle(s) is(are) chosen from the prime area of transmission range of the source vehicle depending on a number of factors including mobility, link quality, buffer size, and number of neighbours. In our work, two multicriteria decision tools named as Fuzzy Analytic Hierarchy Process (FAHP) and Evaluation based on Distance from Average Solution (EDAS) are used together for obtaining criteria weights and utilizing them for ranking the vehicles of the prime area for the selection of relay vehicles. The suggested approach beats existing relay vehicle selection schemes in terms of reachability, redundancy rate, collision rate, delay, PDR, PSR, and throughput, according to simulation results.

A metaheuristic algorithm based on simulated annealing for optimal sizing and techno-economic analysis of PV systems with multi-type of battery energy storage

The battery energy storage unit is one of the main components of hybrid photovoltaic (PV)/battery systems to ensure the economy and reliability of the system to satisfy the electrical loads of green building, especially in remote areas. The main objective of this study is the establishment of a method for optimal design of PV arrangements based on multi-type of battery, in which the annual system cost (ASC) and the loss of load supply probability of the hybrid systems are simultaneously minimized. The hybrid method with simulated annealing and a harmony search methods is suggested in the sizing of the hybrid systems based on three types of energy storage which are lithium-ion, absorbed glass mat, and lead-acid. To calculate the future performance of the hybrid scheme, the effect of reliability index variations and the battery unit cost variations on the sizing problem, ASC, and also loss of power probability are calculated. The simulation results show that the battery storage level increases by increasing the ideal unite of PV and storage values to maintain the system reliability level. Also, it is found that an efficient battery type with the optimal size is techno-economically more beneficial, and leads to greater system reliability.

Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches

• A novel pathway to evaluate optimization algorithms utilizing statistics and ANN. • One-Way ANOVA, Tukey test, and ANN are used to choose the best procedure. • Statistically, the PSO algorithm surpasses all others, but the ANN is better. This study attempts to establish a novel statistical and ANN-based decision-making mechanism for appraising hybrid system optimization strategies. An operational strategy and optimization problem for a hybrid, off-grid PV-wind system based on Nickel Iron battery storage is established in this paper, and an objective function for the system under consideration is presented. Particle swarm optimization, hybrid firefly and harmony search algorithm (HFAHS), Cultural Algorithm, harmony search, and Simulated Annealing are all used to address the optimization issue. A new procedure for selecting the optimal efficient optimization algorithm based on the One-Way ANOVA, the Tukey test, and ANN has been suggested, which allows for effective comparative analysis of algorithms. In the proposed approach, there were just a few steps to determine which algorithm would be the most successful in addressing the specific problem. A comparison of meta-heuristic optimization techniques based on ANN for the optimality of the presented system has been done. Comparing statistical parameters and ANN (i.e., MAE, MAPE, RMSE, and R-squared) to other models verifies the proposed model's efficiency. PSO's R-squared was 99.7%, indicating more accurate predictions. Although the ANN is superior, the PSO algorithm surpasses all other statistically evaluated algorithms.

Modern Techniques for the Optimal Power Flow Problem: State of the Art

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the state of the art (according to the authors, the most pertinent studies). The presented optimization techniques are grouped according to their sources of inspiration, including human-inspired algorithms (harmony search, teaching learning-based optimization, tabu search, etc.), evolutionary-inspired algorithms (differential evolution, genetic algorithms, etc.), and physics-inspired methods (particle swarm optimization, cuckoo search algorithm, firefly algorithm, ant colony optimization algorithm, etc.).

An Improved Harmony Search Approach for Block Placement for VLSI Design Automation

Level of knowledge with the field for VLSI IC Design’s Placement and Routing and in the area of Wireless Communication is rapidly evolving; Hence the process designing in the above fields is critical to assimilate a higher quantity of computation elements or nodes into a very compact size in VLSI Area and also the same is applicable in Wireless Communications to cover more number of nodes in a specific area. Prior to completing the placement, the physical and technical arranging of the computation elements or nodes in the chip area is planned, which is nothing more than Placement step and analogous with coverage of nodes in Wireless communication. Floorplanning must be done properly for reduce given placement area in physical ICDie. The proposed work suggests an ImprovisedHarmonySearch-IHS Optimization Algorithm, which is an Sequential combination of the HarmonySearch (HS) Algorithm and the most effective Particle Swarm Optimization (PSO) Algorithm to achieve the major objective of the VLSI Placement approach and applicable to the field of Wireless Communication. The aim at this point is to reduce the gap between the standard area of the benchmark circuit to the obtained area of the corresponding benchmark circuit. The MATLAB code for the ImprovisedHarmonySearch-IHS Algorithm is recreated, and discoveries are tested for better inspection using standard MCNC, or North Carolina Microelectronics Center benchmark circuits and suggest to adopt the corresponding results to the area of Wireless communication.

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.

An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method

This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the various tolerance ranges of parts. The L16 orthogonal array is used for identifying the effectiveness of the proposed EAUB method through varying the number of bins of the parts of an assembly. Because of qualities such as minimal setting parameters, ease of understanding and implementation, and rapid convergence, the moth–flame optimization (MFO) algorithm is put forward in this work for identifying the optimal combination of bins of the parts of an assembly toward maximizing the percentage of the success rate of making assemblies. Computational results showed a 5.78% improvement in the success rate through the proposed approach compared with the past literature. The usage of the MFO algorithm is justified by comparing the computational results with the harmony search algorithm.

Maximization of the power delivered from permanent magnet synchronous generator wind energy conversion system to the grid based on using moth flame optimization

In recent years, optimization techniques have been developed to improve accuracy and reduce execution time. The moth flame optimization technique (MFO) adapts to moth behavior. This design is based on the moth's transverse orientation. Over long distances, they maintain a fixed angle with respect to the moon at night. They spiral around the lights, however. Moths are trapped in a deadly spiral as the light approaches; eventually, they all converge on it. Furthermore, they are created by artificial light and fly similarly. To maximize the power delivered to the grid, moth flame optimization (MFO) is used to optimize the controller parameters of the wind energy conversion system (WECS). A permanent magnet synchronous generator (PMSG) implements a grid-connected WECS. a grid side converter (GSC) and a generator machine side converter (MSC) are used. A simulation package was used to model the proposed model. PSIM software was used to simulate power circuits and converters. Simulations were done in MATLAB. As a result, the obtained controller coefficients minimize both overshoot and steady-state error. Particle swarm optimization (PSO) and harmony search optimization (HSO) results were compared. MFO is a reliable method.

Real-Time Implementation of an Enhanced PID Controller Based on Ant Lion Optimizer for Micro-Robotics System

Microparticles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper offers a comprehensive comparative study of three meta-heuristic search algorithms for controlling the micro-robotics system with a proportional-integral-derivative (PID) controller. Grey Wolf Optimization (GWO), Harmony Search algorithm (HS) and Ant Lion Optimizer (ALO) are the various techniques that this study adopts. The optimum position control can be obtained by employing the former algorithms with different fitness functions, namely Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error ( (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). In a MATLAB Simulink, each control method was presented, while the experimental measurements were tested and operated by the LabVIEW Software. It is observed that the HS technique achieves the highest values of settling error for both simulation and experimental results among other control approaches, while the ALO approach reduces the settling error by 32.5% compared to former experiments. The results indicate that ALO is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.

Energy and congestion-aware load balanced multi-path routing for wireless sensor networks in ambient environments

In wireless sensor networks (WSNs), a large number of sensor nodes (SNs) equipped with batteries are randomly distributed around an area in order to detect and capture sensor data. In WSNs, congestion is created in the node due to inefficient load balancing and routing. We proposed the congestion-aware load balancing approach considering the highly dynamic traffic scenario. We proposed the traffic rate aware cluster formation for efficient load balancing using a harmony search algorithm. In addition, modified Giraffe kicking optimization has been used for clustering and routing. The load has been aggressively balancing, and the slot has been utilized the slot when no data packets are available with the owner node. The proposed work has been simulated and validated with state-of-the-art congestion-aware algorithms. In our simulation, we validate our proposal on several standard parameters such as network lifetime, throughput, re-transmission frequency, standard deviation of energy consumption etc.

Induction Motor Parameter Estimation Using Harmony Search Algorithm

This study uses the Harmony Search Algorithm to carry out induction motor parameter estimation (HSA). In order to maximize the starting torque and minimize copper losses in the stator and rotor, the parameters rotor resistance R2 and rotor reactance X2 are determined. For parameter estimation of an induction motor, both conventional and intelligent methods are being examined. It is then subjected to the harmony search algorithm to obtain maximum beginning torque and efficiency, respectively.

Multi-objective stock market portfolio selection using multi-stage stochastic programming with a harmony search algorithm

Multi-objective stock market portfolio selection using multi-stage stochastic programming with a harmony search algorithm

Impact of Electric Vehicles on Energy Efficiency with Energy Boosters in Coordination for Sustainable Energy in Smart Cities

The use of electric vehicles (EVs) has recently increased in a smart city environment. With this, the optimal location of the charging station is a great challenge and, hence, the energy efficiency performance (EEP) of an electrical system is important. Ideally, the EEP is realized through passive energy boosters (PEBs) and active energy boosters (AEBs). PEBs require no external resources, and EEP is achieved through altering the network topology and loading patterns, whereas, in AEBs, integrating external energy resources is a must. The EEP has also become dynamic with the integration of an energy storage system (ESS) in a deregulated environment. Customer energy requirement varies daily, weekly, and seasonally. In this scenario, the frequent change in network topology requires modifying the size and location of AEBs. It alters the customers’ voltage profile, loadability margin, and supply reliability when the EV works differently as a load or source. Therefore, a comprehensive EEP analysis with different probabilistic loading patterns, including ESS, must be performed at the planning stage. This work uses a harmony search algorithm to evaluate EEP for AEBs and PEBs, in coordination, when ESS works as a load or source, at four locations, for customers’ and utilities’ benefits.

Predicting the maximum deflection and girder-end rotation of a three-tower suspension bridge under live load: An analytical algorithm

The maximum deflection and girder-end rotation of the stiffening girder for a suspension bridge are key stiffness indicators that need to be verified to secure the structure's performance during the operation. The overall vertical stiffness of a three-tower suspension bridge is significantly reduced due to the mid-tower being constrained in the horizontal direction of the main cable in the main spans with large sag-to-span ratios. Therefore, rapid and accurate evaluation of the vertical stiffness of the three-tower suspension bridge is quite problematic. Based on the deflection theory, this study proposed an analytical expression of the deflection and girder-end rotation of a three-tower suspension bridge. The deflection and girder-end rotation were used as the optimization objectives. Combining the harmony search algorithm with the “death-penalty” function approach, the most unfavorable loading conditions and corresponding maximum values were obtained. The effectiveness of the proposed method was verified by finite element modeling of an exemplary-three-tower suspension bridge. The proposed method had high accuracy, improved calculation efficiency, and wide applicability, which made it lucrative for selecting the optimal structural parameters in the preliminary design of suspension bridges. The dead-to-live load ratio, cable sag-to-span ratio, mid-tower lateral stiffness, side-to-main span ratios, cable’s axial stiffness, and deck’s flexural rigidity were further analyzed. The results indicated that increasing the dead-to-live load ratio and mid-tower stiffness was the most effective method to control maximum deflection and girder-end rotation of stiffening girders.

Watershed-scale optimum livestock distribution and crop pattern planning constrained to the minimum nitrogen and phosphorus load in the runoff.

Sustainable crop and livestock planning encounter serious challenges when tasked with reducing the associated nutrient pollution entering the watershed environment. To overcome these challenges, approaches for specifying optimal crop pattern and livestock distribution to limit the pollution in the catchment are advised. In this research, a simulation-optimization approach is used in which the Soil and Water Assessment Tool (SWAT) is employed for simulating the complex soil-water-plant quantity and quality relations, and the Harmony Search (HS) algorithm linked with SWAT is used to discover the optimal crop pattern and distribution of livestock in the Ilam Dam basin, Iran. In the developed HS-SWAT model, the cultivation area and the number of livestock in SWAT's hydrologic response units (HRU) are the decision variables for maximizing the net benefit obtained from the crop's and livestock's productions, while the nitrate and phosphate calculated in the outflow of the basin are restrained to meet the allowable rates. Results show that the scattered livestock in the basin have a great impact on the generated pollution where about 90% of the nitrate entering the downstream reservoir is the consequence of animal waste. In the optimum state, by reduction of the cultivation area and the number of livestock across the watershed, the concentration of N and P in the surface runoff is reduced significantly to meet the allowable level. According to the results, the HS-SWAT model performance indicates its capability for solving watershed crop pattern and livestock planning problems.

A Hybrid Harmony Search

High Utility Association Rule mining discovers high correlated products in the form of rules. Till date there was no existing algorithm for High Utility Association Rules in a multi-level database. This research work proposes a Hybrid Harmony Search - Antlion Optimization algorithm (HHSAO) to retrieve high utility association rules in multi-level transaction database. First the proposed algorithm implements Harmony Search Algorithm to remove less profitable transactions and then apply Antlion trapping behaviour of Antlion Optimization algorithm to recover the rules from reduced database.The performance of proposed algorithm was compared with adopted existing algorithms like GA, PSO, ACO, AMO, BSO, PeSO, HS and ALO. The experiment was carried out with six datasets. The experimental results prove that the HHSAO algorithm works better.

Implementation of a New Routing Protocol within the Wireless Sensor Networks with the Target of Minimizing Energy Consumption (using a combination of fuzzy algorithm and harmony search)

The Internet of things (IoT) has been following a fast-growing trend in recent years, and the wireless sensor networks account for the basis and, in fact, the regulatory framework of the internet of things regarding their functions. Considering the wide-range applications of these sensors, energy consumption will be one of the main challenges associated with this equipment in the future. The present study mainly deals with the issue of minimizing energy consumption in wireless sensor networks.

A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.

Cantilever Soldier Pile Design: The Multiobjective Optimization of Cost and CO2 Emission via Pareto Front Analysis

In the context of this study, it is focused on the design of cantilever soldier piles under the concept of Pareto optimality with multiobjective analyses of cost and CO2 emission considering the change in the excavation depth, the shear strength parameters of the foundation soil strata, and the unit costs and unit emission amounts of structural materials. Considering this aim, the harmony search algorithm was used as a tool to achieve the integrated effects of the solution variants. The lateral response of the soil mass was determined based on the active Rankine earth pressure theory and the design process was shaped according to the beams on the elastic foundation soil assumption. Moreover, the specification envisaged by the American Concrete Institute (ACI 318-11) was used to control the structural requirements of the design. Pareto front graphs and also design charts were created to achieve the eco- and cost optimization, simultaneously, for the design with arbitrarily selected cases to compare the results of the multiobjective analysis to minimize both the cost and the CO2 emission.

Artificial intelligence based optimization with deep learning model for blockchain enabled intrusion detection in CPS environment

Cyber physical system (CPS) is a network of cyber and physical elements, which interact with one another in a feedback form. CPS approves critical infrastructure and is treated as essential in day to day since it forms the basis of futuristic smart devices. An increased usage of CPSs poses security as a challenging issue and intrusion detection systems (IDS) can be applied for the identification of network intrusions. The latest advancements in the field of artificial intelligence (AI) and deep learning (DL) enables to design effective IDS models for the CPS environment. At the same time, metaheuristic algorithms can be employed as a feature selection approach in order to reduce the curse of dimensionality. With this motivation, this study develops a novel Poor and Rich Optimization with Deep Learning Model for Blockchain Enabled Intrusion Detection in CPS Environment, called PRO-DLBIDCPS technique. The proposed PRO-DLBIDCPS technique initially introduces an Adaptive Harmony Search Algorithm (AHSA) based feature selection technique for proper selection of feature subsets. For intrusion detection and classification, and attention based bi-directional gated recurrent neural network (ABi-GRNN) model is applied. In addition, the detection efficiency of the ABi-GRNN technique has been enhanced by the use of Poor and rich optimization (PRO) algorithm based hyperparameter optimizer, which resulted in enhanced intrusion detection results. Furthermore, blockchain technology is applied for enhancing security in the CPS environment. In order to demonstrate the enhanced outcomes of the PRO-DLBIDCPS technique, a wide range of simulations was carried out on benchmark dataset and the results reported the better outcomes of the PRO-DLBIDCPS technique in terms of several measures.