Harmony Vector Research Articles

Join query optimisation in the distributed databases using a hybrid harmony search and artificial bee colony algorithm

The query optimiser is a vital part of any distributed database mechanism. Reducing the execution period of the query depends on reaching an ideal query execution plan. Due to this issue's NP-hard nature, a hybrid harmony search and an artificial bee colony algorithm can be useful. The harmony is used to call query plans and signify them by S-dimension real vectors. A harmony memory is a place for creating and storing a primary population of harmony vectors. Then, bees explore harmony memory as a food source. The production of a novel nominate harmony out of all query plans in the harmony memory requires a pitch adjustment principle, a memory consideration one, and a random re-initialisation. Lastly, the new candidate vector replaces the worst harmony vector when it works better. The simulation outcomes have indicated that the introduced method reduces the expenses of evaluating a query compared to the harmony search and bee colony optimisation algorithms. However, this method has a longer execution time.

A harmony search variant and a useful constraint handling method for the dynamic economic emission dispatch problems considering transmission loss

This paper proposes a new enhanced harmony search (NEHS) algorithm to solve dynamic economic emission dispatch (DEED) problems. NEHS is a variant of harmony search (HS) algorithm that is inspired by the music phenomenon. NEHS introduces two modifications to increase its competitiveness for the DEED problems. First, three levels of arbitrary distance bandwidths are used to enhance its global search and local search abilities. Second, both the best and the worst harmony vectors are selected to participate in the harmony memory considering operation in the second half of generations, which reaches a good compromise between improving solution quality and overcoming premature convergence. In addition, the improvised harmony vector at each generation is almost infeasible, and a repair technique is devised to drive it to the feasible domain efficiently. The performance of NEHS is evaluated on five DEED instances, and compared with that of the other four state-of-the-art HSs. Experimental results verify that NEHS is preferable in solving the five DEED instances as it can produce better solutions with less computation burden.

Multiobjective Sizing Optimization for Island Microgrids Using a Triangular Aggregation Model and the Levy-Harmony Algorithm

Optimization of island microgrids should configure the module type and size in such a way that multiple objectives can be balanced. This paper presents a bioinspired optimization approach of microgrid sizing, with two salient features. First, the multiple objectives are categorized into four types: reliability, economy, renewable technology, and pollution. We present a triangular aggregation model, which is straightforward and cost effective to compute the fitness. Second, a bioinspired algorithm named Levy-Harmony is developed. We embed the Levy flight into the Harmony vector updating to enhance the global searching ability and, meantime, adopt a bias factor to avoid unnecessary exploration. The searching speed and accuracy are well balanced and improved. The real datasets are used for comparative studies, demonstrating the superiority of the proposed scheme against typical existing approaches.

A self-adaptive multi-objective harmony search based fuzzy clustering technique for image segmentation

Image segmentation can be considered as a problem of clustering since the pixels in the digital image are clustered in term of some evaluation criteria. Generally, clustering technique in image segmentation employs a single objective which can not reach ideal result for various kinds of images. Moreover, fuzzy c-means (FCM) algorithms which determine the fuzzy partition matrix of the data set by solving the clustering problem with conditional constraints and obtain the clustering output, have been verified effective and efficient for image segmentation. In fact, these FCM algorithms still have some shortcomings including: being sensitive to outliers and noise, key parameters need to be adjusted with experience. In view of this, a self-adaptive multi-objective harmony search based fuzzy clustering (SAMOHSFC) technique for image segmentation is proposed in this paper. SAMOHSFC technique encodes several cluster centers in one harmony vector and optimizes multiple objectives. In addition, we consider the spatial information of the image as an attribute of the input data set besides the attribute of gray information of input image in the SAMOHSFC. Superiority of the proposed algorithm over three classic segmentation algorithms has been verified for a synthetic and two real images from quantitative and visual aspect. In the experiment, the effect of different kinds of spatial information on the segmentation performance of the SAMOHSFC is analyzed.

A Differential-Based Harmony Search Algorithm With Variable Neighborhood Search for Job Shop Scheduling Problem and Its Runtime Analysis

Job shop scheduling problem (JSSP) has drawn a lot of attention as it is one of the vital combinational optimization problems in manufacturing systems. In this paper, a differential-based harmony search (DHS) algorithm with variable neighborhood search (VNS) is proposed for solving JSSP with the objective of minimized makespan. Since the standard harmony search algorithm is constructed for global optimization problems, the smallest position value is introduced to map a harmony vector to an active schedule. The active decoding scheme is employed to improve the search efficiency of DHS. In the pitch-adjustment process, the best individual of the current harmony memory is employed to accelerate the convergence speed. After the pitch-adjustment process, the differential-based enhanced mechanism is designed to maintain the diversity of the population. The modified VNS, which is based on the blocks on the critical path, is embedded into DHS to search for a better solution around the current harmony vector. Besides, the runtime of DHS is analyzed according to the level-based theorem. Compared with various HS-based algorithms and other state-of-the-art algorithms on a set of typical benchmark instances, the DHS is superior to the compared algorithms in terms of solution quality, convergence speed, and stability. The DHS lays a solid foundation for solving optimization problems of expert and intelligent systems.

A hybrid harmony search algorithm with efficient job sequence scheme and variable neighborhood search for the permutation flow shop scheduling problems

The permutation flow shop scheduling problem (PFSSP), one of the most widely studied production scheduling problems, is a typical NP-hard combinatorial optimization problem. In this paper, a hybrid harmony search algorithm with efficient job sequence mapping scheme and variable neighborhood search (VNS), named HHS, is proposed to solve the PFFSP with the objective to minimize the makespan. First of all, to extend the HHS algorithm to solve the PFSSP effectively, an efficient smallest order value (SOV) rule based on random key is introduced to convert continuous harmony vector into a discrete job permutation after fully investigating the effect of different job sequence mapping schemes. Secondly, an effective initialization scheme, which is based on NEH heuristic mechanism combining with chaotic sequence, is employed with the aim of improving the solution’s quality of the initial harmony memory (HM). Thirdly, an opposition-based learning technique in the selection process and the best harmony (best individual) in the pitch adjustment process are made full use of to accelerate convergence performances and improve solution accuracy. Meanwhile, the parameter sensitivity is studied to investigate the properties of HHS, and the recommended values of parameters adopted in HHS are presented. Finally, by making use of a novel variable neighborhood search, the efficient insert and swap structures are incorporated into the HHS to adequately emphasize local exploitation ability. Experimental simulations and comparisons on both continuous and combinatorial benchmark problems demonstrate that the HHS algorithm outperforms the standard HS algorithm and other recently proposed efficient algorithms in terms of solution quality and stability.

Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation

AbstractIn this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.

Scheduling Semiconductor Wafer Fabrication Using a New Harmony Search Algorithm Based on Receipt Priority Interval

For the scheduling problem of Semiconductor wafer fabrication (SWF), a new Dispatching rule based on the load balance (DRLB) is proposed. Further, a new Harmony search (HS) algorithm based receipt priority interval (HS rpi) is presented to minimize the mean cycle time. A kind of chaotic sequence is used as the harmony vector. Then, a conversion method is designed to convert the real number harmony vector to the mixed vector representing the priorities of all receipts and the algorithm parameters. In order to increase the algorithm robustness and decrease the scale of the scheduling problem, based on receipt priority interval and DRLB, we give a special conversion method used to convert the above mixed vector to the solution of the scheduling problem of SWF. Computational simulations based on the practical instances validate the proposed algorithm.

Clustering with a Novel Global Harmony Search Algorithm for Image Segmentation

A state-feedback based harmony search (SFHS) algorithm to clustering data is presented to solve the problems that the traditional evolutionary-based clustering algorithms easily trap into the local optimum. Firstly, the state-feedback mechanism is introduced into HS algorithm, and the harmony memory difference metric is defined to adaptively adjust harmony memory considering rate and pitch adjusting bandwidth, which makes the convergence and efficiency of the harmony search (HS) algorithm improved obviously. Secondly, in the SFHS-based clustering algorithm, the decision variables in the harmony vector represent cluster centers, and the harmony vector represents a partition of data, the best partition of data can be obtained by updating the harmony memory. Finally, a novel validity metric is presented to determine the right number of clusters. Simulation experiments have been carried out on remote sensing images and animal images, and the relevant results are compared with the ANT-based clustering algorithm and the traditional HS-based clustering algorithms, it shows that the SFHS-based clustering algorithm has better convergence.

An Improved Global Harmony Search Algorithm for the Identification of Nonlinear Discrete-Time Systems Based on Volterra Filter Modeling

This paper describes an improved global harmony search (IGHS) algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS) algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL) is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes.

An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems

This paper proposes a modified harmony search (MHS) algorithm with an intersect mutation operator and cellular local search for continuous function optimization problems. Instead of focusing on the intelligent tuning of the parameters during the searching process, the MHS algorithm divides all harmonies in harmony memory into a better part and a worse part according to their fitness. The novel intersect mutation operation has been developed to generate new -harmony vectors. Furthermore, a cellular local search also has been developed in MHS, that helps to improve the optimization performance by exploring a huge search space in the early run phase to avoid premature, and exploiting a small region in the later run phase to refine the final solutions. To obtain better parameter settings for the proposed MHS algorithm, the impacts of the parameters are analyzed by an orthogonal test and a range analysis method. Finally, two sets of famous benchmark functions have been used to test and evaluate the performance of the proposed MHS algorithm. Functions in these benchmark sets have different characteristics so they can give a comprehensive evaluation on the performance of MHS. The experimental results show that the proposed algorithm not only performs better than those state-of-the-art HS variants but is also competitive with other famous meta-heuristic algorithms in terms of the solution accuracy and efficiency.

Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

A modified harmony search algorithm with co-evolutional control parameters (DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual (i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.

A self-adaptive harmony PSO search algorithm and its performance analysis

Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHPSOS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the PSO algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHPSOS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHPSOS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHPSOS outperforms original HS and the other related algorithms in terms of the solution quality and the stability.

Development of a new approach, evolutionary harmony search algorithm, for the LPO problem

This paper proposes a new variant of harmony search (HS) algorithm i.e. evolutionary harmony search (EHS), for exploiting in the loading pattern optimization (LPO) problem. The main innovations of EHS are the consideration of current best harmony vector in creating the new solution vector in any iteration and applying a pitch adjustment approach using a mutation strategy borrowed from the realm of the differential evolution (DE) algorithms, both with dynamic probabilities. Reactor core pattern optimization has been done using EHS for two test cases including KWU PWR and VVER-440. In order to represent the EHS capability to gain improved results in the LPO problem, a comparison is performed between results of EHS and a recent developed HS algorithm i.e. self-adaptive global best harmony search (SGHS). Numerical results show a major and distinctive enhancement of the proposed approach, EHS, in obtaining convergent results in comparison to SGHS approach. As a result, I can recommend the checking of EHS performance in other optimization problems.

An Improved Harmony Search Algorithm with Application

In classical harmony search algorithm, only one harmony vector is obtained in each of iteration, which affects its search ability. We propose an improve harmony search algorithm in this paper. In our approach, the number of harmony vectors obtained in each of iteration is equivalent to the population size, and all newly generated harmony vectors are put into the harmony memory array. Then, all harmony vectors are sorted by descending order of the fitness, and the first half individuals are served as the next generation of populations. Experimental results show that our approach is obviously superior to the classical one under the same iteration steps and the same running time, which reveals that our approach can effectively generate the excellent individuals approximating the global optimal solution and enhance the optimization ability of classical harmony search algorithm.

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

A Novel Discrete Global-Best Harmony Search Algorithm for Solving 0-1 Knapsack Problems

In order to better solve discrete 0-1 knapsack problems, a novel global-best harmony search algorithm with binary coding, called DGHS, is proposed. First, an initialization based on a greedy mechanism is employed to improve the initial solution quality in DGHS. Next, we present a novel improvisation process based on intuitive cognition of improvising a new harmony, in which the best harmony of harmony memory (HM) is used to guide the searching direction of evolution during the process of memory consideration, or else a harmony is randomly chosen from HM and then a discrete genetic mutation is done with some probability during the phase of pitch adjustment. Third, a two-phase repair operator is employed to repair an infeasible harmony vector and to further improve a feasible solution. Last, a new selection scheme is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems.

Global best harmony search algorithm with control parameters co-evolution based on PSO and its application to constrained optimal problems

A global best harmony search algorithm with control parameters co-evolution based on particle swarm optimization (PSO-CE-GHS) is proposed. In PSO-CE-GHS, two control parameters, i.e. harmony memory considering rate and pitch adjusting rate, are encoded to be a symbiotic individual of original individual (i.e. harmony vector). Harmony search operators are applied to evolve the original population. And, PSO is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in PSO-CE-GHS, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. The proposed PSO-CE-GHS algorithm has been applied to various benchmark functions and constrained optimal problems. The results show that the proposed algorithm can find better solutions when compared to HS and its variants.

A hybrid harmony search algorithm for the flexible job shop scheduling problem

In this paper, a novel hybrid harmony search (HHS) algorithm based on the integrated approach, is proposed for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize makespan. First of all, to make the harmony search (HS) algorithm adaptive to the FJSP, the converting techniques are developed to convert the continuous harmony vector to a kind of discrete two-vector code for the FJSP. Secondly, the harmony vector is mapped into a feasible active schedule through effectively decoding the transformed two-vector code, which could largely reduce the search space. Thirdly, a resultful initialization scheme combining heuristic and random strategies is introduced to make the initial harmony memory (HM) occur with certain quality and diversity. Furthermore, a local search procedure is embedded in the HS algorithm to enhance the local exploitation ability, whereas HS is employed to perform exploration by evolving harmony vectors in the HM. To speed up the local search process, the improved neighborhood structure based on common critical operations is presented in detail. Empirical results on various benchmark instances validate the effectiveness and efficiency of our proposed algorithm. Our work also indicates that a well designed HS-based method is a competitive alternative for addressing the FJSP.

A novel approach for the reconfiguration of distribution systems considering the voltage stability margin

In recent years, the problem of optimum reconfiguration in distribution systems (DSs) has been a task that must be solved in an optimal manner. This paper presents a new approach for the optimal reconfiguration of DSs based on a hierarchical 2-stage optimization problem to improve the power system voltage stability margin and reduce losses incorporating the constraints. The mentioned problem has been modeled as a nonlinear and multiobjective optimization problem. It uses the ability of the developed harmony search algorithm (HSA) as the first stage of the proposed optimization problem to reach the best network configuration. This reconfiguration algorithm starts with a radial topology by a theoretical approach that is based on the graph concept and matroid theory. These concepts are used in order to propose new intelligent HSAs to form a new harmony vector that is well dedicated to the DS reconfiguration problem. Thus, all of the resulting individuals after forming a new harmony vector are claimed to be feasible configurations. Moreover, the presented approach is valid and avoids tedious mesh checks for the topology constraint validation. In the second stage of the proposed approach, the voltage stability index is calculated to evaluate the static voltage stability security margin for each reconfiguration pattern. Hence, a toolbox has been developed to recognize the loadability limit of DSs based on the Lagrangian optimization method. Finally, the proposed method establishes a tradeoff between the security index and power losses to reach a coordinated reconfiguration pattern. To demonstrate the validity of the proposed method, the simulations are carried out on 33- and 69-bus IEEE DSs. The proposed method is finally compared to some previous techniques used by other authors. The results show a good enhancement in the security margin and smaller power losses with considerably less computation effort. To validate the proposed method, the results that were obtained from the HSA are compared with the particle swarm optimization algorithm to ascertain its effectiveness.