Global-best Harmony Search Research Articles

Experimental validation of double-fed induction generator in oscillating water column using circulatory system-based optimization

Abstract This paper validates the optimal operation for a grid-connected double-fed induction generator (DFIG) in an oscillating water column power plant (OWCPP). This study presents a novel optimization technique called the circulatory system-based optimization (CSBO) approach to develop six adaptive fuzzy logic controllers (AFLCs) with 30 parameters and compare them to chaotic-billiards optimization (C-BO) and genetic algorithm (GA). The proposed controller is also compared with a proportional–integral differential (PID) controller based on a self-adaptive global-best harmony search (SGHS). CSBO-based AFLCs are fully investigated under different scenarios and experimented with using a real-time interface DSP1104. The results of using CSBO–AFLCs revealed a fast time response, fast convergence, less overshoot and minimal error compared with those achieved with C-BO–AFLC, SGHS–PID and GA–AFLC during different case studies. The CSBO-based AFLCs ensure maximum power from the DFIG in an OWCPP and enhance dynamic response with very low errors. The results show that the CSBO shows better power tracking by 25% as compared with C-BO, by 45% when compared with the GA and by 56% when compared with PID. Moreover, the integral absolute errors of six controllers are investigated to demonstrate the feasibility of CSBO–AFLC. The root mean square of the errors of six controllers using CSBO is improved by 68.27% when compared with GA, by 22.57% when compared with C-BO and by 38.42% when compared with PID. These indicators demonstrate the feasibility of CSBO when compared with other algorithms with the same OWCPP.

Hybrid Learning Moth Search Algorithm for Solving Multidimensional Knapsack Problems

The moth search algorithm (MS) is a relatively new metaheuristic optimization algorithm which mimics the phototaxis and Lévy flights of moths. Being an NP-hard problem, the 0–1 multidimensional knapsack problem (MKP) is a classical multi-constraint complicated combinatorial optimization problem with numerous applications. In this paper, we present a hybrid learning MS (HLMS) by incorporating two learning mechanisms, global-best harmony search (GHS) learning and Baldwinian learning for solving MKP. (1) GHS learning guides moth individuals to search for more valuable space and the potential dimensional learning uses the difference between two random dimensions to generate a large jump. (2) Baldwinian learning guides moth individuals to change the search space by making full use of the beneficial information of other individuals. Hence, GHS learning mainly provides global exploration and Baldwinian learning works for local exploitation. We demonstrate the competitiveness and effectiveness of the proposed HLMS by conducting extensive experiments on 87 benchmark instances. The experimental results show that the proposed HLMS has better or at least competitive performance against the original MS and some other state-of-the-art metaheuristic algorithms. In addition, the parameter sensitivity of Baldwinian learning is analyzed and two important components of HLMS are investigated to understand their impacts on the performance of the proposed algorithm.

Extremum hybrid intelligent-inspired models for accurate predicting mechanical performances of turbine blisk

In complex aeroengine structures, it is necessary to consider multi-physical loads involving vibrational, structural, aerodynamical and heat loads for safe design. Turbine blisk commonly involves multi-failure modes such as stress, strain, deformation, fatigue, and creep under multi-physical loads during operation, so that efficient and accurate simulating approach is one of main issues in structural safety design. Machine learning (ML) approaches using artificial intelligence (AI) are workable to improve the simulation efficiency of mechanical responses under nonlinear dynamic structural analysis. In this work, hybrid artificial neural network (ANN) models are proposed to simulate the failure modes of turbine blisk. The accuracy of AI-based ANN is discussed using six music-inspired optimization algorithms named as harmony search (HS), improved HS (IHS), global-best HS (GHS), improved GHS (IGHS), adaptive GHS (AGHS) and Gaussian GHS (GGHS). Herein, these six music-inspired optimization algorithms are employed to find the undetermined coefficients and hyperparameters for the ANN models, and two adjusting strategy is introduced to explore the optimal values of the undetermined coefficients and hyperparameters in the IGHS, AGHS and GGHS. These hybrid models are used to perform the approximation of dimensional mechanical capacities such as stress, strain and deformation of aeroengine structures. These hybrid models are compared by using tendency, accuracy and agreement for failure modes of turbine system. It was conducted that the music-inspired basis harmony optimization procedures can provide the acceptable nonlinear connection between input and output responses of aeroengine turbine blisk in training phase of ANN models. The GGHS algorithm shows superior performance with the highest accuracy and tendency among other HS optimization algorithms.

Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks.

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively.

Automatic Calibration for CE-QUAL-W2 Model Using Improved Global-Best Harmony Search Algorithm

CE-QUAL-W2 is widely used for simulating hydrodynamics and water quality of the aquatic environments. Currently, the model calibration is mainly based on trial and error, and therefore it is subject to the knowledge and experience of users. The Particle Swarm Optimization (PSO) algorithm has been tested for automatic calibration of CE-QUAL-W2, but it has an issue of prematurely converging to a local optimum. In this study, we proposed an Improved Global-Best Harmony Search (IGHS) algorithm to automatically calibrate the CE-QUAL-W2 model to overcome these shortcomings. We tested the performance of the IGHS calibration method by simulating water temperature of Devils Lake, North Dakota, which agreed with field observations with R2 = 0.98, and RMSE = 1.23 and 0.77 °C for calibration (2008–2011) and validation (2011–2016) periods, respectively. The same comparison, but with the PSO-calibrated CE-QUAL-W2 model, produced R2 = 0.98 and Root Mean Squared Error (RMSE) = 1.33 and 0.91 °C. Between the two calibration methods, the CE-QUAL-W2 model calibrated by the IGHS method could lower the RMSE in water temperature simulation by approximately 7–15%.

Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials

Having the advancement in automated fiber placement (AFP) and three-dimensional (3D) printing, fabricating novel composites reinforced with curvilinear fibers, namely variable stiffness composites (VSCs), have gradually become feasible. Contrasted to the traditional straight fiber-reinforced composites, VSCs possess significantly enhanced mechanical properties and increased mechanical performance. However, the optimization of a VSC panel is still a challenging problem, which can be attributed to the high non-convexity of the feasible design space and highly involved computation resources. To accelerate the design and optimization of VSC structures, in this study, we propose an effective and efficient adaptive surrogate-based harmony search algorithm (ASBHSA). The VSC is modeled using the fiber path methodology through which the fiber trajectory is generated by a mathematical formulation. In addition, the manufacturability and quality of the product are guaranteed by considering the curvature constraint. For the ASBHSA, a distance-based infill criterion in combination with a radial basis function (RBF) is proposed to guide the adaptive sampling. The global best harmony search (GHS) with a good ability to find the global minimum is integrated into the developed optimization framework. After solving some popular benchmark optimization problems as well as making comparisons of the optimal designs of VSCs with those in published works, the proposed optimization framework is demonstrated being able to effectively and efficiently find the global minimum. Then, a series of designs and optimizations of laminated VSC plates under various boundary conditions and different loading conditions are carried out systematically. The reported interesting findings may provide some insights and guidance in the design of laminated VSC composites. Moreover, our proposed optimization model provides a useful tool for solving various kinds of optimization problems.

Novel hybrid machine leaning model for predicting shear strength of reinforced concrete shear walls

Accurate prediction of the ultimate shear capacity of reinforced concrete shear walls (RCSWs) is essential for robust design of buildings under seismic and wind loads. However, the shear capacity of RCSWs depends on multiple complex design variables characterized by diverse geometric and materials properties. Thus, a powerful modeling framework is required. In this paper, a hybrid artificial intelligence model is proposed for predicting the ultimate shear capacity of RCSWs named artificial neural network (ANN) coupled with adaptive harmony search optimization (AHS) algorithm. Different statistical metrics were used to compare the performances of the ANN model coupled with AHS (ANN-AHS) to three existing empirical relations and two ANN models combined with harmony search (ANN-HS) and global-best harmony search (ANN-GHS). Results show that the proposed ANN-AHS achieved superior performance in modelling the shear strength of RCSWs compared to ANN-HS and ANN-GHS models. The soft-computing models have proven to be more accurate than existing empirical relations.

Adaptation, Comparison, and Improvement of Metaheuristic Algorithms to the Part-of-Speech Tagging Problem

Part-of-Speech Tagging (POST) is a complex task in the preprocessing of Natural Language Processing applications. Tagging has been tackled from statistical information and rule-based approaches, making use of a range of methods. Most recently, metaheuristic algorithms have gained attention while being used in a wide variety of knowledge areas, with good results. As a result, they were deployed in this research in a POST problem to assign the best sequence of tags (roles) for the words of a sentence based on information statistics. This process was carried out in two cycles, each of them comprised four phases, allowing the adaptation to the tagging problem in metaheuristic algorithms such as Particle Swarm Optimization, Jaya, Random-Restart Hill Climbing, and a memetic algorithm based on Global-Best Harmony Search as a global optimizer, and on Hill Climbing as a local optimizer. In the consolidation of each algorithm, preliminary experiments were carried out (using cross-validation) to adjust the parameters of each algorithm and, thus, evaluate them on the datasets of the complete tagged corpus: IULA (Spanish), Brown (English) and Nasa Yuwe (Nasa). The results obtained by the proposed taggers were compared, and the Friedman and Wilcoxon statistical tests were applied, confirming that the proposed memetic, GBHS Tagger, obtained better results in precision. The proposed taggers make an important contribution to POST for traditional languages (English and Spanish), non-traditional languages (Nasa Yuwe), and their application areas.

Self-Adaptive Global-Best Harmony Search Algorithm-Based Airflow Control of a Wells-Turbine-Based Oscillating-Water Column

The Harmony Search algorithm has attracted a lot of interest in the past years because of its simplicity and efficiency. This led many scientists to develop various variants for many applications. In this paper, four variants of the Harmony search algorithm were implemented and tested to optimize the control design of the Proportional-Integral-derivative (PID) controller in a proposed airflow control scheme. The airflow control strategy has been proposed to deal with the undesired stalling phenomenon of the Wells turbine in an Oscillating Water Column (OWC). To showcase the effectiveness of the Self-Adaptive Global Harmony Search (SGHS) algorithm over traditional tuning methods, a comparative study has been carried out between the optimized PID, the traditionally tuned PID and the uncontrolled OWC system. The results of optimization showed that the Self-Adaptive Global Harmony Search (SGHS) algorithm adapted the best to the problem of the airflow control within the wave energy converter. Moreover, the OWC performance is superior when using the SGHS-tuned PID.

A congestion-aware decision-driven architecture for information-centric Internet-of-Things applications

This paper introduces a decision-driven architecture for the information-centric Internet-of-Things (IoT) scenarios. The hallmark of this work is the conjugate of the awareness about the congestion situations along with the quality of the decisions’ supporting information. Controlling the incoming decisions’ upsurge is accomplished on a fashion that respects their degree of emergency. Wherefore, the Active Weighted Random Early Detection (AWRED) algorithm is integrated into the autonomic admission module to underpin the congestion control concept. Afterwards, an adaptive information-centric scheduling module is proposed in order to devise an optimized information-aware execution schedule for the admitted real-time decisions. The proposed scheduling algorithm is augmented with a Global best Harmony search (GHS) based amending approach. This integrated scheduling concept empowers the proposed algorithm from tuning the schedules of the admitted real-time decisions such that the completeness opportunities of decisions are elevated without sacrificing the rest architecture objectives. Simulations results demonstrate the robustness of the proposed architecture performance against various congestion degrees, an essential feature in a highly dynamic environment as the IoT. Furthermore, it has been proved that the proposed architecture significantly surpasses state-of-art approaches in terms of both quality of information (QoI) and quality of service (QoS) obligations especially during congested situations.

Harmony Search Method with Global Sharing Factor Based on Natural Number Coding for Vehicle Routing Problem

This paper proposes an improved Harmony Search algorithm, and gives the definition of the Global Sharing Factor of the Harmony Search (HS) algorithm. In the definition, the number of creations of the HS algorithm is applied to the sharing factor and calculated. In this algorithm, the natural harmony encoding method is used to encode the initial harmony, and the total path length of all vehicles is taken as the optimization objective function. A new harmony generation strategy is proposed as follows: each tone component in an evolution is calculated separately using the new learning strategy and update strategy. In the calculation process, the tone component is judged by whether it needs to be adjusted according to the adjustment strategy. In this way, the problems of singularity and randomness of the new harmony generation strategy of basic HS are solved to improve the diversity of algorithm solutions. Then, a new Harmony Search method with Global Sharing Factor based on natural number coding and decoding for the Vehicle Routing Problem (GSF-HS-VRP) is proposed. The improved Global Sharing Factor-Harmony Search-Vehicle Routing Problem (GSF-HS-VRP) algorithm is applied to capacity-limited vehicle path optimization problems compared with the HS, Improved Harmony Search (IHS), Global-best Harmony Search (GHS), and Self-adaptive Global Best Harmony Search (SGHS) algorithms. The small-scale data and Solomon examples were adopted as the experimental data. Compared with the other four algorithms, the GSF-HS-VRP algorithm has the shortest running time, more rapid convergence speed, and higher efficiency. In the multi-vehicle test, with the increase of the number of vehicles, the optimized path of the vehicle is more satisfied in relation to the actual needs of customers. The results showed that this method could effectively improve the optimization performance of the capacity-limited vehicle routing problem.

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

The calibration of traffic-flow simulation models continues to be a significant problem without a generalized, comprehensive, and low-cost solution. Existing calibration approaches either have not explicitly addressed the multi-objective characteristics of the problem or determining their hyperparameters requires significant effort. In addition, statistical evaluation of alternative solution algorithms is not performed to ensure dominance and stability. This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic traffic-flow simulation models. The adapted MOGBHS provides five key capabilities for solving the proposed problem including 1) consideration of multiple objectives, 2) easily extendable to memetic versions, 3) simultaneous consideration of continuous and discrete variables, 4) efficient ordering of no dominated solutions, 5) relatively easy tuning of hyperparameters, and 6) easily parallelization to maximize exploration and exploitation without increasing computing time. Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem. The efficiency and effectiveness of the algorithms were tested based on convergence, minimization of errors, calibration criterion, and two statistical nonparametric tests. The proposed approach dominated all alternative algorithms in all cases and provided the most stable and diverse solutions.

Non-gradient probabilistic Gaussian global-best harmony search optimization for first-order reliability method

The performances of first-order reliability method (FORM) are highly important owing to its accuracy and efficiency in the structural reliability analysis. In the gradient methods-based sensitivity analysis, the iterative formula of FORM is established using the gradient vector which it may not compute for some structural problems with discrete or non-continuous performance functions. In this study, the probabilistic Gaussian global-best harmony search (GGHS) optimization is implemented to search for the most probable point in the structural reliability analysis. The proposed GGHS approach for reliability analyses is performed based on two main adjusted processes using the random Gaussian generation. The accuracy and efficiency of the GGHS are compared with original harmony search (HS) algorithm and three modified versions of HS as improved HS, global-best HS, and improved global-best HS based on a mathematical and three structural problems. The obtained results illustrated that the PGGHS is more efficient than other modified versions of HS and provides the accurate results for discrete performance functions compared to original FORM-based gradient method.

Enhanced self-adaptive global-best harmony search without any extra statistic and external archive

Harmony search is a music-inspired optimization algorithm. Our findings reveal that the common uniform randomization selection in the harmony search algorithm is less efficient than the Gaussian mutation to explore the global optimal solution, regardless of solving unimodal or multimodal problems under the same parameters settings. To evaluate the effectiveness of a given search strategy, a general measurement that can apply to other algorithms is also proposed. To enhance the search efficiency and effectiveness, a self-adaptive global-best harmony search algorithm is developed. The proposed algorithm takes full advantage of the valuable information hidden in harmony memory to devise a high-performance search strategy and integrates a self-adaptive mechanism to develop a parameter-setting-free technique. Moreover, it is as simple and straightforward to implement as the canonical harmony search algorithm. It does not require any extra statistic and external archive. It well maintains the interesting and distinctive framework of the original version in the evolutionary computation domain. The experimental results show that the proposed algorithm significantly outperforms the recent adaptive variant of the harmony search algorithm and achieves the strongly competitive performances compared with other state-of-the-art adaptive evolutionary algorithms. The proposed algorithm is also successfully applied into the real-world space trajectory optimization problem.

Building a Nasa Yuwe Language Corpus and Tagging with a Metaheuristic Approach

Nasa Yuwe is the language of the Nasa indigenous community in Colombia. It is currently threatened with extinction. In this regard, a range of computer science solutions have been developed to the teaching and revitalization of the language. One of the most suitable approaches is the construction of a Part-Of-Speech Tagging (POST), which encourages the analysis and advanced processing of the language. Nevertheless, for Nasa Yuwe no tagged corpus exists, neither is there a POS Tagger and no related works have been reported. This paper therefore concentrates on building a linguistic corpus tagged for the Nasa Yuwe language and generating the first tagging application for Nasa Yuwe. The main results and findings are 1) the process of building the Nasa Yuwe corpus, 2) the tagsets and tagged sentences, as well as the statistics associated with the corpus, 3) results of two experiments to evaluate several POS Taggers (a Random tagger, three versions of HSTAGger, a tagger based on the harmony search metaheuristic, and three versions of a memetic algorithm GBHS Tagger, based on Global-Best Harmony Search (GBHS), Hill Climbing and an explicit Tabu memory, which obtained the best results in contrast with the other methods considered over the Nasa Yuwe language corpus.

A multiobjective bilevel approach based on global-best harmony search for defining optimal routes and frequencies for bus rapid transit systems

The operation of a Bus Rapid Transit System (BRTS) requires solving complex design problems including among others the selection of routes and the corresponding frequency of service. One challenge is the large number of potential solutions with multiple routes having many frequency options. Furthermore, the design should at least seek to minimize the total time spend by users in the system, and the operational cost to pursue sustainability and profitability. The literature includes a wide variety of approaches to solve this Transit Network Design and Frequency Setting Problem (TNDFSP) for a BRTS. This study proposes a single framework that simultaneously considers restrictions and objectives of the users and operator of the system. A Multiobjective Global-Best Harmony Search (MOGBHS) heuristic algorithm was implemented and tested successfully. The algorithm is based on three main components: 1) Global-Best Harmony Search as a heuristic optimization strategy, 2) ordering of non-dominated solutions as a multiobjective optimization strategy, and 3) simulation of discrete events to evaluate solutions. A bilevel implementation of MOGBHS was adopted. At the external level, the algorithm searched the best configuration of routes while at the internal level, the algorithm searched the best frequency for a solution for a specific route. Experiments were performed using a simulation model of an actual BRTS located in Pereira, Colombia, known as Megabus. Routes and frequencies were searched for this BRTS by minimizing waste bus capacities (operation costs) and minimizing users’ travel time (maximizing satisfaction). Results using the proposed algorithm where superior to those obtained using comparable alternatives including NSGA-II and MOEA/D.

An adaptive response surface method and Gaussian global-best harmony search algorithm for optimization of aircraft stiffened panels

For the engineering optimization problems characterized by high computational cost and multiple local optima, the combination use of surrogate model and meta-heuristic algorithm is increasingly popular. In this paper, a bi-loop optimization framework of stiffened panels is proposed to search the global optimum, which includes an adaptive response surface method (ARSM) loop and a Gaussian global-best harmony search (GGHS) loop, aiming to improve the global optimization capacity in an efficient manner. The ARSM loop involves an inner loop to select the data set for training the response surface function, where the spherical data set can provide an accurate prediction of the optimum condition compared to the original RSM. Typical aircraft stiffened panels in NASA are employed to demonstrate the effectiveness of the proposed framework. Results demonstrate that the proposed ARSM has higher prediction accuracy of weight and buckling load compared to the traditional RSM, and the optimum design of GGHS combined with ARSM has the lowest relative error by comparison with different harmony search algorithms. Finally, a fast prediction model of aircraft stiffened panel is developed, which can provide approximated optimum conditions without detailed optimization process.

QoS-aware optimal and automated semantic web service composition with user’s constraints

Automated semantic web service composition is one of the critical research challenges of service-oriented computing, since it allows users to create an application simply by specifying the inputs that the application requires, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from artificial intelligence planning to optimization algorithms. However no approach so far has focused on handling three composition dimensions simultaneously, producing solutions that are: (1) fully functional (i.e., fully executable) by using a mechanism of semantic matching between the services involved in the solutions, (2) are optimized according to non-functional quality-of-service (QoS) measurements, and (3) respect global QoS constraints. This paper presents a novel approach based on a Harmony Search algorithm that addresses these three dimensions simultaneously through a fitness function, to select the optimal or near-optimal solution in semantic web service composition. In our approach, the search space is modeled as a planning-graph structure which encodes all the possible composition solutions for a given user request. To improve the selection process we have compared the original Harmony Search algorithm with its recently developed variants Improved Harmony Search (IHS) algorithm and Global Best Harmony Search (GHS) algorithm. An experimentation of the approach conducted with an extended version of the Web Service Challenge 2009 dataset showed that: (1) our approach is efficient and effective to extract the optimal or near-optimal composition in diverse scenarios; and (2) both variants IHS and GHS algorithms have brought improvements in terms of fitness and execution time.

Semi-self-adaptive harmony search algorithm

This paper proposes a semi-self-adaptive harmony search algorithm (SSaHS) with the self-adaptive adjustment of the bandwidth and the elitist learning strategy of particle swarm optimization. SSaHS employs a self-adaptive adjusting strategy for with the difference between the maximum and minimum components in the harmony memory as the bandwidth. It can dynamically adjust the bandwidth for the specific problem to strengthen local exploitation ability and improve the accuracy of optimization results. Comparison results show that the semi-self-adaptive harmony search algorithm can find better solutions when comparing with both basic harmony search algorithm and several enhanced harmony search algorithms, including an improved harmony search, a global-best harmony search and a novel global harmony search.

A cooperative-competitive master-slave global-best harmony search for ANN optimization and water-quality prediction

The artificial neural network (ANN) is one of the most accurate and commonly used machine-learning techniques and can learn even complex data by employing metaheuristic algorithms. Harmony search (HS) is a metaheuristic algorithm that imitates the process by which musicians tune their instruments to achieve perfect harmony. Global-best harmony search(GHS) is an effective variant of the HS algorithm that borrows the concept of gbest (globalbest) from particle-swarm optimization (PSO) to improve the performance of HS. Employing a multi-population technique improves the convergence of the algorithm. The master-slave technique is one of the most powerful multi-population techniques. This paper proposes a cooperative-competitive master-slave multi-population GHS (CC-GHS) to train the ANN. To provide the proposed CC-GHS algorithm with strong abilities in both exploration and exploitation, a competitive master-slave strategy (Com-GHS)is interacted with a cooperative master-slave strategy(Coo-GHS). A probabilistic variable is employed to achieve a good balance between cooperativeness and competitiveness. The method is tested on benchmark classification and time-series prediction problems, and statistical analyses demonstrate the ability of the proposed method. The CC-GHS is also applied to a real-world water-quality prediction problem with promising results.