Conventional Harmony Search Research Articles

Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis.

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.

Weight Optimization of Discrete Truss Structures Using Quantum-Based HS Algorithm

Recently, a new field that combines metaheuristic algorithms and quantum computing has been created and is being applied to optimization problems in various fields. However, the application of quantum computing-based metaheuristic algorithms to the optimization of structural engineering is insufficient. Therefore, in this paper, we tried to optimize the weight of the truss structure using the QbHS (quantum-based harmony search) algorithm, which combines quantum computing and conventional HS (harmony search) algorithms. First, the convergence performance according to the parameter change of the QbHS algorithm was compared. The parameters selected for the comparison of convergence performance are QHMS, QHMCR, QPAR, ϵ, and θr. The selected parameters were compared using six benchmark functions, and the range for deriving the optimal convergence performance was found. In addition, weight optimization was performed by applying it to a truss structure with a discrete cross-sectional area. The QbHS algorithm derived a lower weight than the QEA (quantum-inspired evolutionary algorithm) and confirmed that the convergence performance was better. A new algorithm that combines quantum computing and metaheuristic algorithms is required for application to various engineering problems, and this effort is essential for the expansion of future algorithm development.

Size and Topology Optimization of Truss Structures Using Quantum-Based HS Algorithm

With the development of various metaheuristic algorithms, research cases that perform weight optimization of truss structures are steadily progressing. In particular, due to the possibility of developing quantum computers, metaheuristic algorithms combined with quantum computation are being developed. In this paper, the QbHS (Quantum based Harmony Search) algorithm was proposed by combining quantum computation and the conventional HS (Harmony Search) algorithms, and the size and topology optimization of the truss structure was performed. The QbHS algorithm has the same repetitive computational structure as the conventional HS algorithm. However, the QbHS algorithm constructed QHM (Quantum Harmony Memory) using the probability of Q-bit and proposed to perform pitch adjusting using the basic state of Q-bit. To perform weight optimization of truss structures using the proposed QbHS algorithm, 20 bar, 24 bar, and 72-bar truss structures were adopted as examples and compared with the results of the QE (Quantum Evolutionary) algorithm. As a result, it was confirmed that the QbHS algorithm had excellent convergence performance by finding a lower weight than the QE algorithm. In addition, by expressing the weight optimization results of the truss structure with an image coordinate system, the topology of the truss structure could be confirmed only by the picture. The results of this study are expected to play an important role in future computer information systems by combining quantum computation and conventional HS algorithms.

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement

This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India.

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 low- and high-rise building structures by Tribe-Harmony Search algorithm

In this paper, optimum design of building structures is conducted by metaheuristic algorithms due to the shortcomings of the conventional design methods in providing economical designs. The Harmony Search (HS) is utilized as the main algorithm which was developed based on the musical process of searching for the optimal condition of harmony to produce an appropriate search approach for design optimization purposes. Besides, the Tribe-Harmony Search (Tribe-HS) algorithm is also proposed for the first time in this paper to improve the performance of the HS algorithm which divides the HS’s searching phase into three distinct phases called “tribes”, lead the primary algorithm to prioritize global search in the early iterations while resolving local search in the later iterations. Three building structures with 135, 3860, and 8272 structural members are used as design examples to demonstrate the suggested method’s capacity to solve challenging optimization problems. The recommended method’s overall performance is compared to that of the conventional Harmony Search algorithm and ten alternative metaheuristic algorithms using a total of 30 independent runs in each instance for statistical reasons. The findings demonstrated that the suggested method outperformed the other metaheuristics for the study design instances.

An Enhanced Fuzzy Control Strategy for Low-Level Thrusters in Marine Dynamic Positioning Systems Based on Chaotic Random Distribution Harmony Search

The required control force vector is distributed by the thrusters in marine dynamic positioning system (DPS) to obtain the desired thrust and angle of each thruster. The thrust of the thruster is mapped to the speed of the thruster, and the low-level thrust controller adjusts the speed of the thruster to achieve the vessel’s dynamic position. Based on the previous research, the permanent magnet synchronous motor (PMSM) is selected as the low-level driving motor, and it is combined with the propeller to form the low-level thrusters for the DPS. The fuzzy control strategy is selected for the PMSM controller design, and the proposed chaotic random distribution harmony search (CRDHS) algorithm is used to optimize the fuzzy controller rules’ weights. The proposed CRDHS employs a chaotic map for rule weight adaptation in order to prevent the conventional harmony search to get stuck on local solutions. By adjusting the weights of each fuzzy rule via CRDHS, more consistent control performance is achieved. The required fuzzy output and the fuzzy controller are used for control of the PMSM. Simulation results show that under load disturbance, the fuzzy controller based on CRDHS has better control performance.

LBest-HS algorithm based concurrent [formula omitted] adaptive control for non-Linear systems

This paper proposes a new approach for designing stable hybrid L1 adaptive controller employing lbest topological model of harmony search (HS) algorithm. The proposed design approach guarantees desired stability and simultaneously provides satisfactory tracking performance for a class of non-linear systems. The design methodology for the controller utilizes the meta-heuristic global search feature of HS algorithm and the local search phenomenon of L1 adaptive control strategy in tandem. The paper also analytically describes the superiority of lbest topological model compared to the conventional HS algorithm in terms of convergence phenomenon, when hybridized with L1 adaptive control. The proposed hybrid control methodology has been implemented for benchmark simulation case studies and real-time experimentation to demonstrate its usefulness.

HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs

Clustering objective reasons scalability, fault tolerance, data aggregation or fusion, load balancing of cluster heads, stabilized network topology, maximal network lifetime, increased connectivity, reduced routing delay, collision avoidance and utilizing sleeping schemes in wireless sensor networks. Load balanced clustering effectively organize the network into a connected hierarchy. Clustering is a discrete problem that can have more than one solution under different operating constraints. In this scenario, meta-heuristic algorithms are found suitable because they give set of solutions in acceptable time constraints. In the literature, several analytical and meta-heuristic approaches have been developed for load balanced clustering. In this paper, a novel harmony search based energy efficient load balanced clustering algorithm is presented and it is tested on a large sample network. Results demonstrated that the proposed approach has faster convergence and gives reliable and efficient load balanced clustering as compared to conventional harmony search algorithm (HSA) and several other methods in the literature. Moreover, the robustness of the proposed approach is also verified for different cases of fixed and variable parameters of HSA.

A differential-based harmony search algorithm for the optimization of continuous problems

The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings and the initialization of the Harmony Memory (HM). To address these issues, this paper presents a new variant of the HS algorithm, which is called the DH/best algorithm, for the optimization of globally continuous problems. The proposed DH/best algorithm introduces a new improvisation method that differs from the conventional HS in two respects. First, the random initialization of the HM is replaced with a new method that effectively initializes the harmonies and reduces randomness. Second, the conventional pitch adjustment method is replaced by a new pitch adjustment method that is inspired by a Differential Evolution (DE) mutation strategy known as DE/best/1. Two sets of experiments are performed to evaluate the proposed algorithm. In the first experiment, the DH/best algorithm is compared with other variants of HS based on 12 optimization functions. In the second experiment, the complete CEC2014 problem set is used to compare the performance of the DH/best algorithm with six well-known optimization algorithms from different families. The experimental results demonstrate the superiority of the proposed algorithm in convergence, precision, and robustness.

Fuzzy harmony search algorithm based optimal power flow for power system security enhancement

Abstract This paper proposes the integration of fuzzy logic system with harmony search algorithm (FHSA) to find the optimal solution for optimal power flow (OPF) problem in a power system. The objective of the method is to minimize the total fuel cost of thermal generating units having quadratic cost characteristics and severity index (SI). The generator active power, generator bus voltage magnitude, transformer taps, VAR of shunts and the reactance of thyristor controlled series capacitor (TCSC) are taken as the control variables. The adjustment of proposed algorithm parameters such as pitch adjustment rate (PAR) and bandwidth (BW) is done through fuzzy logic system (FLS). The effectiveness of the proposed method has been tested on the standard IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems in MATLAB environment and their results are compared with conventional harmony search algorithm (HSA) and other heuristic methods reported in the literature recently.

An improved variant of the conventional Harmony Search algorithm

The Harmony Search algorithm (HS) has been used for optimization in different fields, and despite the relative short time it has been around, it already has many variants. This article presents a new modification of HS, based on variable parameters, which is able to yield better results than previously reported data, and with the additional benefit of not requiring prior knowledge of the maximum number of iterations. In this research, a comparison is made with the original HS algorithm, and with its improved version (i.e. IHS), finding that the proposed variants not only reduce convergence time of the algorithm, but they also increase its precision. Some commonly used benchmark functions were used as a testing scenario, and the performance of the novel approach is evaluated for an objective function in up to 1000D, where it was found to converge appropriately. These findings are important since they indicate that the proposed version could be used for different kinds of optimization problems, thus allowing a broader use of the HS algorithm.

Optimization and Parametric Modelling to Support Conceptual Structural Design

The paper describes software combining parametric geometric modeling with a version of the harmony search method, modified to support multimodal structural optimization. Researchers have recognized the potential of population-based optimization methods, such as genetic algorithms, to support multimodal optimization: that is, generating a diverse range of good alternative solutions, rather than a single best solution. Among these methods is the harmony search method, which has been demonstrated to be efficient in many unimodal structural optimization problems. The paper describes a new version of the harmony search method, implemented as an assembly within Bentley's Generative Components, enabling high-level control of geometry. The new method is demonstrated on an bridge supported by two inclined parabolic arches, a structure where GC controls a complex geometry with a single variable. Comparative studies of the example find that the new method is more effective than conventional harmony search in consistently finding multiple good solutions.

Harmony Search Method for Multimodal Size, Shape, and Topology Optimization of Structural Frameworks

This paper describes and demonstrates modifications of the harmony search method to support multimodal structural optimization. Several researchers have recognized the potential of population-based optimization methods, such as genetic algorithms and particle swarm optimization, to support multimodal optimization, that is, generating a range of good alternative solutions, rather than a single best solution. Among these population-based methods is the harmony search method, which has been demonstrated to be efficient and effective in many unimodal structural optimization problems. Toward the goal of making the harmony search method more effective in multimodal optimization, this paper describes a new strategy for generating solutions called close-harmony improvisation, and a new strategy for replacing solutions called local replacement. Examples demonstrate the effect of the two strategies used individually and in tandem. The discussion compares results with conventional harmony search and finds that close-harmony improvisation consistently improves the fitness of the search results, although the effect is sometimes mild, whereas local replacement is quite effective in increasing the diversity of the search result.