Modified Harmony Search Algorithm Research Articles

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.

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

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.

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.

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.

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.

Feature Selection for Colon Cancer Detection Using K-Means Clustering and Modified Harmony Search Algorithm

This paper proposes a feature selection method that is effective in distinguishing colorectal cancer patients from normal individuals using K-means clustering and the modified harmony search algorithm. As the genetic cause of colorectal cancer originates from mutations in genes, it is important to classify the presence or absence of colorectal cancer through gene information. The proposed methodology consists of four steps. First, the original data are Z-normalized by data preprocessing. Candidate genes are then selected using the Fisher score. Next, one representative gene is selected from each cluster after candidate genes are clustered using K-means clustering. Finally, feature selection is carried out using the modified harmony search algorithm. The gene combination created by feature selection is then applied to the classification model and verified using 5-fold cross-validation. The proposed model obtained a classification accuracy of up to 94.36%. Furthermore, on comparing the proposed method with other methods, we prove that the proposed method performs well in classifying colorectal cancer. Moreover, we believe that the proposed model can be applied not only to colorectal cancer but also to other gene-related diseases.

Arrangement optimization of a novel three dimensional multiphase flow imaging device employing modified harmony search algorithm

Gas–liquid two-phase flow is a typical flow. Its bubble characteristic measurement is of great importance on studying the flow mechanism and guiding the practical fluid mechanical engineering. In this paper, a novel three dimensional (3D) multiphase flow imaging device was designed and optimized to measure the transparent object that has an opaque object in the center of the 3D observed area. Its mathematical model was built and the constraints were defined based on the geometrical relationship and design requirements. Based on the original harmony search (HS) algorithm, a modified harmony search algorithm (MHS-MC) for improving both the performance on the global optima of the multi-modal problems and the convergence performance of the constrained optimization problems was integrated and applied to optimize the arrangement of the single-camera-multi-mirror device. As a case study, the 3D multiphase flow imaging method was applied in the 3D reconstruction of the cavitation bubble cluster inside a water hydraulic valve. The statistics of the Pareto data show the good performance of the MHS-MC algorithm. And the cavitation experimental results testify the effectiveness of the proposed MHS-MC algorithm. The cavitation bubble cluster can be reconstructed with quite high precision.

Stochastic scheduling of a renewable-based microgrid in the presence of electric vehicles using modified harmony search algorithm with control policies

Modern power systems are seeking for alternative energy sources will less emission to conventional fossil-fuel power plant to mitigate the global concerns on environmental issues. On one hand, renewable energies have turned to the first choice of system operators. On the other hand, the system should provide the required infrastructure to appropriately accommodate such energy sources. In this respect, microgrids (MG) would provide the needed conditions for integrating renewable energy sources (RESs). Thus, the optimal energy management of such systems in the presence of highly uncertain renewable power generation is of great importance. Accordingly, this paper provides a stochastic programming framework for the optimal scheduling of an MG equipped with RESs and plug-in electric vehicles (PEVs). The power sources considered include a wind energy system in the form of wind turbine (WT), a solar photovoltaic (PV) system, a fuel cell (FC), a microturbine (MT), besides a battery storage system (BSS). The mentioned problem is formulated as a single-objective optimization problem aimed at minimizing the total operating cost over the scheduling period. The MG is considered in the grid-connected mode where it can transact power with the upstream system. The uncertainty of the problem is due to the intermittent power output of the wind energy system and the PV unit, as well as uncertain behavior of the EV owners in charging/discharging their vehicles. The proposed stochastic optimization problem is the solved using an effective and efficient optimization algorithm named “modified harmony search (MHS) algorithm”. Finally, the simulation results are discussed and the superior performance of the suggested algorithm is verified through making a comprehensive comparison with some well-known methods.

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Compared with rules in the form of `IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

Improved Dynamic Harmony Search Optimization for Economic Dispatch Problems with Higher Order Cost Functions

This paper presents a modified harmony search algorithm with dynamically varying bandwidth, named improved dynamic harmony search algorithm (IDHSA) for economic load dispatch (ELD) problems with higher cost functions. The economic load dispatch problem aims to schedule power outputs of the generating units to meet the system load demand at minimum cost while satisfying the equality and inequality constraints. In the IDHS algorithm, the key difference from the conventional HS algorithm is that bandwidth (BW) operator changes dynamically at every iteration. The IDHS algorithm is tested with two different cases of power systems without and with transmission losses. The obtained results prove the accuracy and the effectiveness of the IDHS algorithm in determining the best solution compared than other optimization methods recently published in the literature.

Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem.

Robust optimization framework for dynamic distributed energy resources planning in distribution networks

This study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved.

A modified harmony search algorithm applied to capacitor placement of radial distribution networks considering voltage stability index

In power system, capacitor placement of distribution networks is a complex combinatorial optimisation problem. In this paper, in order to effectively solve optimal capacitor placement (OCP), an aggregate harmony search (AHS) optimiser, a stochastic population-based metaheuristic algorithm, is proposed which introduces a new pitch adjustment mechanism for harmony search (HS). The objective function contains three goals: 1) minimisation of the power losses; 2) minimisation of the capacitor installation cost; 3) improvement of the voltage stability index at the weakest bus. On two case studies, 23 kV nine-section feeder and 33-bus radial system, simulation results show that the proposed AHS not only finds more accurate results than the other investigated HS variants and particle swarm optimisation (PSO) techniques but also has the best robustness. Promising performance of AHS makes this technique as an efficient alternative to solve capacitor placement problem.

A modified harmony search algorithm applied to capacitor placement of radial distribution networks considering voltage stability index

In power system, capacitor placement of distribution networks is a complex combinatorial optimisation problem. In this paper, in order to effectively solve optimal capacitor placement (OCP), an aggregate harmony search (AHS) optimiser, a stochastic population-based metaheuristic algorithm, is proposed which introduces a new pitch adjustment mechanism for harmony search (HS). The objective function contains three goals: 1) minimisation of the power losses; 2) minimisation of the capacitor installation cost; 3) improvement of the voltage stability index at the weakest bus. On two case studies, 23 kV nine-section feeder and 33-bus radial system, simulation results show that the proposed AHS not only finds more accurate results than the other investigated HS variants and particle swarm optimisation (PSO) techniques but also has the best robustness. Promising performance of AHS makes this technique as an efficient alternative to solve capacitor placement problem.

Quick Reconstruction of Arbitrary Pipeline Defect Profiles From MFL Measurements Employing Modified Harmony Search Algorithm

This paper proposes a quick and accurate method for reconstructing defect profiles in pipelines from magnetic flux leakage (MFL) signals employing a modified harmony search (MHS) algorithm. To improve the quality of the initial harmony memory, a multiple selection opposition-based learning strategy is presented in the MHS algorithm. Moreover, the pitch adjusting rate is set to increase linearly, and the distance bandwidth ( bw ) is designed to be dynamically adjusted to shorten the calculation time. A specially designed modified sum-squared error with weights is proposed and employed as the fitness function to improve the fitting degree between the reference and predicted signals in the defect response segment. The weights are determined by the characteristics of reference signals. We demonstrate the adequacy of the performance of the proposed model using both simulated and real MFL signals. We also present comparative results of the proposed model and other methods. The results prove that the proposed method can achieve higher efficiency while maintaining excellent accuracy.

Modified harmony search algorithm for combined economic emission dispatch of microgrid incorporating renewable sources

Nowadays, there is a growing interest in the microgrid systems with a high penetration of renewable sources. In this paper, the modified harmony search (MHS) algorithm is proposed to solve the combined economic emission dispatch (CEED) problem of the microgrid taking into account the solar and wind power cost functions. The proposed algorithm can be derived by not only adjusting the parameters but also improving the structure and operation of the original harmony search (HS) algorithm. The solution of the CEED problem of the microgrid taking into account the solar and wind power cost functions is obtained for different scenarios using the MHS algorithm and some recently published algorithms. The results of all scenarios show the effectiveness of the MHS algorithm over other published algorithms employing same data.