Modified Harmony Search Research Articles

Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches.

Brain Tumor Detection and Classification Using IFF-FLICM Segmentation and Optimized ELM Model

Brain cancer deaths are significantly increased in all categories of aged persons due to the abnormal growth of brain tumor tissues in the brain. The death rate can be controlled by accurate early stage brain tumor diagnosis. The detection and classification of brain tumors play a crucial role in early diagnosis and treatment planning. Brain tumor detection and classification have become challenging and time-consuming for domain-specific radiologists and pathologists in medical image analysis. So, automatic detection and classification are essential to reduce the time of diagnosis. In recent years, machine learning classifiers have played an essential role in automatically classifying brain tumors. In this research, an approach based on an improved fuzzy factor fuzzy local information C means (IFF-FLICM) segmentation and hybrid modified harmony search and sine cosine algorithm (MHS-SCA) optimized extreme learning machine (ELM) is proposed for brain tumor detection and classification. The IFF-FLICM algorithm is utilized to accurately segment the brain’s magnetic resonance (MR) images to identify the tumor regions. The Mexican hat wavelet transform is employed for feature extraction from the segmented images. The extracted features from the segmented regions are fed into the MHS-SCA-ELM classifier for classification. The MHS-SCA is proposed to optimize the weights of the ELM model to improve the classification performance. Five distinct multimodal and unimodal benchmark functions are considered for optimization to demonstrate the robustness of the proposed MHS-SCA optimization technique. The image Dataset-255 is considered for this study. The quality measures such as SSIM and PSNR are considered for segmentation. The proposed IFF-FLICM segmentation achieved a peak signal-to-noise ratio (PSNR) of 37.24 dB and a structural similarity index (SSIM) of 0.9823. The proposed MHS-SCA-based ELM model achieved a sensitivity, specificity, and accuracy of 98.78%, 99.23%, and 99.12%. The classification performance results of the proposed MHS-SCA-ELM model are compared with MHS-ELM, SCA-ELM, and PSO-ELM models, and the comparison results are presented.

Optimization of electrical resistivity survey utilizing modified harmony search algorithm to predict anomalous zone ahead of tunnel faces

Predicting anomalies ahead of a tunnel face is essential to ensure construction safety and efficiency during tunneling. This study proposes an optimization system to estimate the characteristics of anomalies ahead of a tunnel face. The developed system can estimate the location, the thickness and the electrical resistivity of the anomaly using inverse analysis based on the modified harmony search algorithm with resistivity measurement. To verify the efficacy of the developed system, laboratory chamber tests were conducted by simulating ground formations with faults. The experimental results indicated that the developed system had substantial prediction performance with a short data analysis time. Notably, the location of an anomaly was relatively well predicted compared to the other properties, which is crucial information that can prevent potential risks. Therefore, the system could predict anomaly characteristics and enable the appropriate management of potential risks.

Hybrid and Modified Harmony Search Optimization application in economic load dispatch with integrated renewable source

Hybrid and Modified Harmony Search Optimization application in economic load dispatch with integrated renewable source

Design Optimization of PCI Girders: A Parametric Study

This study investigates the effect of superstructure configuration on the optimum design of slab on Precast I (PCI) girder bridges. For this purpose, more than 20,000 bridge cases of varying superstructure configurations are considered to investigate the effects of various superstructure parameters such as girder spacing, span length, slab thickness and girder types on the optimum design of slab on PCI girder bridges. PCI girders are designed conforming to the AASHTO LRFD for flexure using stress limits at the service limit state, then checked at ultimate for flexure and shear using factored loads at the strength limit state. A modified harmony search optimization algorithm is used to obtain optimum bridge design parameters using standard AASHTO PCI girders according to these AASHTO LRFD requirements. Those girders are designed taking into consideration geometrical constraints, stress constraints and constraints related to the conformity of the design with the AASHTO LRFD code. Various sensitivity analysis are performed to investigate the effect of different geometrical factors on the design of the girders, and easy-to-use design aids were developed. The outcomes of this study may facilitate the bridge engineers to choose optimum design parameters such as girder types and spacing as well as number strands for a certain span length before the design of slab on PCI girder bridges.

Output-only identification of a simplified onshore wind turbine model using a modified harmony search algorithm

Output-only identification of a simplified onshore wind turbine model using a modified harmony search algorithm

Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm

In an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. EVs are powered by photovoltaic (PV), battery, and ultracapacitor (UC) systems. The overall results of this arrangement are an increase in travel distance; a reduction in battery size; improved reaction, especially under overload; and an extension of battery life. Improved results allow the energy to be used efficiently, provide a comfortable ride, and require fewer energy sources. In this research, energy management between the PV system and the hybrid energy storage system (HESS), including the battery, and UC are discussed. The energy management control algorithms called Artificial Neural Network (ANN) and Aquila Optimizer Algorithm (AOA) are proposed. The proposed combined ANN–AOA approach takes full advantage of UC while limiting the battery discharge current, since it also mitigates high-speed dynamic battery charging and discharging currents. The responses’ behaviors are depicted and viewed in the MATLAB simulation environment to represent load variations and various road conditions. We also discuss the management among the PV system, battery, and UC to achieve the higher speed of 91 km/h when compared with existing Modified Harmony Search (MHS) and Genetic Algorithm-based Proportional Integral Derivative (GA-PID). The outcomes of this study could aid researchers and professionals from the automotive industry as well as various third parties involved in designing, maintaining, and evaluating a variety of energy sources and storage systems, especially renewable ones.

Optimization of prestressed concrete bridge girder section using a modified harmony search algorithm

Precast I girders provide rapid construction and structural simplicity, making them an increasingly popular choice for bridge construction. The current standard sections adopted by the American Association of State Highway and Transportation Officials (AASHTO) are not SI friendly and may be improved utilizing increasingly efficient metaheuristic optimization algorithms. Thus, this research study aims to obtain the optimum shape of Precast I (PCI) bridge girders commonly used in bridge construction due to various advantages. The optimization problem is solved using a modified metaheuristic optimization algorithm considering a number of constraints that are stipulated by the AASHTO LRFD code requirements. To show the capabilities of the utilized modified optimization algorithm, a comparison is performed with its baseline counterpart. The structural design optimization of bridge superstructures is performed with the objective of minimizing the total cost of the resulting superstructure. Consequently, the most economical shape of the PCI girders is obtained. The results of this analysis are then compared AASHTO and the California Department of Transportation (Caltrans) PCI girders. The new I girder sections exhibits 13–17% and 5–16% better structural efficiency factor (ρ) as well as 14–19% and 4–21% structural efficiency ratio (α) than Caltrans and AASHTO girders, respectively.Thus, new sections for PCI girders are developed having various heights to facilitate more economical design of bridges having various geometric properties such as span length, girder spacing and slab thickness.

Power leakage minimization in time‐modulated linear antenna array with optimized switching strategies employing modified harmony search algorithm

Time-modulated linear antenna array (TMLAA) with periodic time modulation of antenna elements radiates infinite inevitable harmonic beams. The infinite harmonic sideband radiation is a wastage of power unless the power is exploited. This paper aims to minimize inevitable power wastage in the harmonic beams radiated from TMLAA by employing an optimized periodic timing sequence. The optimization of the timing sequence is conducted employing harmony search (HS) to suppress the power wastage, However, conventional HS achieves an acceptable sidelobe level (SLL) below – 30 dB. Nevertheless, a modified harmony search algorithm (MHSA) is proposed to attain more power in the desired fundamental beam. The algorithm is validated using an array of 4-, 10-, and 16-element uniform half-wavelength spacing TMLAA. The optimization is conducted on the ON and OFF time instants and the real current of the elements. The simulation results exhibit that HS and MHSA perform potentially alike to suppress the SLL of fundamental below –30 dB. However, the further suppression of the sideband levels employing MHSA justifies the modification proposed in this work. The MHSA performance in terms of radiation parameters is compared with the HS and also with previous works to show the potentiality of the modification.

Design optimization and optimal power management of standalone solar-hydrogen system using a new metaheuristic algorithm

Optimal configuration models and efficient energy management strategies are developed based on a standalone energy system, including hydrogen fuel cell and photovoltaic (PV) in green buildings. To optimize a hybrid scheme, integer decision variables and continuous decision variables are considered as the number of scheme components. To the optimal size of the system, the objective function is defined based on the system annual cost and loss of power supply probability (LPSP) concept for reliability. To define the optimal solution to the optimization problem, the use of three modified harmony search (MHS) algorithms are proposed, namely, MHSLD, MHSWF, and MHSLWF. These methods are compared with modified and original HS algorithms. Furthermore, three loss of power supply probability values are introduced (1 %. 2 %, and 3 %) for the optimal power management. In this regard, the effect of the PV price and reservoir tank cost variation on the justification of the systems is investigated. The simulation results reveal that the MHSWF performs with high accuracy and robustness as it obtains the minimal values of the investigated indexes for different desired reliability, βL, (1 %, 2 %, and 3 %) in all simulation runs obtained. Therefore, the ranking order of the search power of the algorithms is MHSWF > MHSLD > MHSLWF > MHS > HS. For the desired βL with 1 %, MHSWF obtained the minimal SAC values and achieved cost savings of 0.8 %, 6.1 %, 11 %, and 20.2 % as compared with four other methods, and the saving reached up to 13.2 % in the costs for other βL values. The sensitivity analyses have been performed on the results of the modeling which reveals that by the increase of the desired βL (from 1 % to 3 %), the values of PV areas, and the number of hydrogen tanks decrease by 5.4 % and 31 %, respectively, while the value of SAC decreases by 31 %.In different desired βL, by decreasing the PV panel area cost, the value of SAC based on LPSP decreases, and by reducing the hydrogen tank cost of the system by 46 %, the SAC of the system based on LPSP decreased by 39 %.

A New Approach of BLDC Motor Using Fuzzy Fractional Order PID

In order to manage the speed of brushless DC (BLDC) motors, this research presents a novel hybrid control method that simultaneously regulates the DC bus voltage of the inverter and the BLDC motor reference current. A fractional-order PID (FOPID) controller manages the BLDC motor reference current, and a fuzzy logic controller manages the inverter DC bus voltage. A modified harmony search (HS) metaheuristic technique is developed for adjusting the FOPID controller parameters. Three separate working scenarios—no load, varying load, and varying speed—are used to test the motor's capabilities. Run the proposed controller at a high speed to verify its efficacy. The suggested hybrid control method has also been put to the test. weighed against FOPID and fuzzy-based speed control techniques The outcomes demonstrate the effectiveness of the suggested control. approach enables more accurate speed control over a wide region.

A modified harmony search for the T-single machine scheduling problem with variable and flexible maintenance

A common assumption in the scheduling literature provides that machines are always available, while often they have to be stopped because of preventive maintenance operations. Inspired by a real-life manufacturing context, this paper deals with the single machine scheduling problem with a flexible/variable maintenance to be executed within a specific time window. Differently from other studies in the literature, a set of non-resumable jobs with sequence dependent setup-times and non-zero release dates have to be scheduled along with a maintenance activity whose duration is a non-decreasing function of its starting time, with the objective of minimizing the total tardiness (T-problem). A mathematical model of the problem is proposed and, due to the high computational complexity, several heuristics have been implemented and tested with the aim of using some of them to make metaheuristic algorithms more effective and computationally efficient. Hence, a modified harmony search, powered by several reinforcement strategies and even equipped with a self-adaptive calibration mechanism, has been developed and compared with a set of additional algorithms proposed by the literature. Numerical results reveal that the modified harmony search outperforms the competing algorithms and no performance loss arises by adopting the self-adaptive mechanism. Finally, a proper sensitivity analysis allows assessing the impact of several influencing factors on the total tardiness minimization.

Modified Harmony Search Algorithm-Based Optimization for Eco-Friendly Reinforced Concrete Frames

Cost and CO2 are two factors in the optimum design of structures. This study proposes a modified harmony search methodology for optimization of reinforced concrete beams with minimum CO2 emissions. The optimum design was developed in detail by considering all possible combinations of variable loads, including dynamic force resulting from earthquake motion. Moreover, time-history analyses were performed, and requirements of the ACI-318 building code were considered in the reinforced concrete design. The results show that the optimum design based on CO2 emission minimization is greatly different from the optimum cost design results. According to these results, using recycled members provides a sustainable design.

Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

A Decomposition‐Based Harmony Search Algorithm for Multimodal Multiobjective Optimization

Multimodal multiobjective optimization problem (MMOP) is a special kind of multiobjective optimization problem (MOP) with multimodal characteristics, where multiple different Pareto optimal sets (PSs) map to the same Pareto optimal front (PF). To handle MMOPs, a decomposition‐based harmony search algorithm (called MOEA/D‐HSA) is devised. In MOEA/D‐HSA, multiple individuals who are assigned to the same weight vector form a subpopulation for finding multiple different PSs. Then, an environmental selection method based on greedy selection is designed to dynamically adjust the subpopulation scale for keeping the population diversity. Finally, the modified harmony search algorithm and elite learning strategy are utilized to balance the diversity and convergence of the population. Experimental results on the CEC 2019 test suite reveal that MOEA/D‐HSA has superior performance than a few state‐of‐the‐art algorithms.

Optimal Design of Damped Structure with Inerter System Based on Modified Harmony Search Algorithm

An inerter system can amplify the deformation of its internal energy dissipation device, thereby improving the efficiency of energy dissipation and shock absorption. This is the so‐called damping enhancement mechanism, one of the key mechanisms of the inerter system. Although the theoretical framework for damping enhancement of inerter systems has been established, the implementation of this principle for the design of an inerter system requires solving a complicated constrained optimization problem, which is not easy to be figured out using traditional approaches. To obtain valid design results through a lucid and robust method, it is proposed to optimize the damping parameters through a metaheuristic algorithm named harmony search algorithm in order to maximize the damping enhancement degree of the inerter system with the satisfaction of structural performance. First, the closed‐form seismic response solutions of a single‐degree‐of‐freedom (SDOF) structure with an inerter system are derived based on the theory of random vibration. Then, the mathematical expression of the constrained optimization problem is established. Due to the inefficiency of the original harmony search algorithm to solve the constrained optimization problem, the algorithm is modified by introducing a new harmony generating method and an adaptive strategy for parameter adjustment. The modified harmony search algorithm is compiled to solve the optimal design problem of the inerter system. The algorithm is verified by designing a structure with an inerter system. It is found that the number of iterations and time consumption until convergence required by the modified harmony search algorithm can be reduced by about 20%∼90% compared with the original algorithm, which confirms the effectiveness of the modified algorithm. The results of dynamic analyses show that the structure have achieved the preset performance demands under different cases and the damping enhancement characteristic of the inerter system is fully utilized.

Modified Harmony Search for a Truck Loading Problem Application

We must decide how to load boxes in a truck, to avoid damaging during the transportation and simplifying the delivery to multiple clients. We propose a constructive heuristic and a modified Harmony Search metaheuristic, using the open dimension approach, the length in this case. The constructive heuristic, based on the Wall Building approach, provides a very fast initial solution for the metaheuristic. The selection algorithm, guiding the metaheuristic, considers the optimization of the multi-client features originally. The results show the effectiveness of the metaheuristic in searching solutions that improve the considered features, in reasonable times according to the truck's load planning.

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

Optimal robust adaptive fuzzy backstepping control of electro-hydraulic servo position system

In this paper, an optimal robust adaptive fuzzy backstepping control is presented to the position control of the electro-hydraulic servo (EHS) system in the presence of structured and unstructured uncertainties. Initially, the robust control using the backstepping technique is presented to overcome the existing uncertainties in the dynamic equations. Mathematical proof demonstrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability. Then, to overcome the chattering problem, a very simple fuzzy approximator is presented where it approximates the bounds of the uncertainties. Although the proposed robust fuzzy backstepping control has a desirable performance, it has no mathematical analysis to prove the stability of the closed-loop system. Therefore, to solve this problem, the proposed fuzzy approximator has been transformed into a one-law adaptive fuzzy approximator with a single-input single-output fuzzy rule. Mathematical analysis illustrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability under the proposed robust adaptive fuzzy backstepping control. Furthermore, a novel modified harmony search algorithm (MHSA) has been developed, by using the original harmony search algorithm (OHSA) as an optimization technique, to achieve the optimal values of the membership functions and the control coefficients. Finally, a comparative study has been conducted between the proposed control scheme under the MHSA and the OHSA, and other existing advanced control approaches to verify the effectiveness of the proposed control. Results show that the proposed control scheme under the MHSA can suppress the chattering problem and reduce the disturbances effectively while ensuring that the performance is tracked.

Modified Harmony Search Algorithm for Resource-Constrained Parallel Machine Scheduling Problem with Release Dates and Sequence-Dependent Setup Times

This research focuses on the problem of scheduling a set of jobs on unrelated parallel machines subject to release dates, sequence-dependent setup times, and additional renewable resource constraints. The objective is to minimize the maximum completion time (makespan). To optimize the problem, a modified harmony search (MHS) algorithm was proposed. The parameters of MHS are regulated using full factorial analysis. The MHS algorithm is examined, evaluated, and compared to the best methods known in the literature. Four algorithms were represented from similar works in the literature. A benchmark instance has been established to test the sensitivity and behavior of the problem parameters of the different algorithms. The computational results of the MHS algorithm were compared with those of other metaheuristics. The competitive performance of the developed algorithm is verified, and it was shown to provide a 42% better solution than the others.