Standard Harmony Search Algorithm Research Articles

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

Multidirectional harmony search algorithm for solving integer programming and minimax problems

Integer programming and minimax problems are essential tools in solving various problems that arise in data mining and machine learning such as multi-class data classification and feature selection problems. In this paper, we propose a new hybrid harmony search algorithm by combining the harmony search algorithm with the multidirectional search method in order to solve the integer programming and minimax problems. The proposed algorithm is called multidirectional harmony search algorithm (MDHSA). MDHSA starts the search by applying the standard harmony search for numbers of iteration then the best-obtained solution is passing to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard harmony search algorithm. The proposed algorithm is balancing between the global exploration of the harmony search algorithm and the deep exploitation of the multidirectional search method. MDHSA algorithm is tested on seven integer programming problems and 15 minimax problems and compared against 12 algorithms for solving integer programming problems and 11 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer programming and minimax problems in reasonable time.

Multidirectional harmony search algorithm for solving integer programming and minimax problems

Integer programming and minimax problems are essential tools in solving various problems that arise in data mining and machine learning such as multi-class data classification and feature selection problems. In this paper, we propose a new hybrid harmony search algorithm by combining the harmony search algorithm with the multidirectional search method in order to solve the integer programming and minimax problems. The proposed algorithm is called multidirectional harmony search algorithm (MDHSA). MDHSA starts the search by applying the standard harmony search for numbers of iteration then the best-obtained solution is passing to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard harmony search algorithm. The proposed algorithm is balancing between the global exploration of the harmony search algorithm and the deep exploitation of the multidirectional search method. MDHSA algorithm is tested on seven integer programming problems and 15 minimax problems and compared against 12 algorithms for solving integer programming problems and 11 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer programming and minimax problems in reasonable time.

Cylindricity Error Evaluation Based on an Improved Harmony Search Algorithm

The cylindricity error is one of the basic form errors in mechanical parts, which greatly influences the assembly accuracy and service life of relevant parts. For the minimum zone method (MZM) in international standards, there is no specific formula to calculate the cylindricity error. Therefore, the evaluation methods of the cylindricity error under the MZM have been widely concerned by international scholars. To improve the evaluation accuracy and accelerate the iteration speed of the cylindricity, an improved harmony search (IHS) algorithm is proposed and applied to compute the cylindricity. On the basis of the standard harmony search algorithm, the logistic chaotic initialization is introduced into the generation of initial solution to improve the quality of solutions. During the iterative process, the global and local search capabilities are balanced by adopting the par and bw operators adaptively. After each iteration, the Cauchy mutation strategy is adopted to the best solution to further improve the calculation precision of the IHS algorithm. Finally, four test functions and three groups of cylindricity error examples were applied to validity verification of the IHS algorithm, the simulation test results show that the IHS algorithm has advantages of the computing accuracy and iteration speed compared with other traditional algorithms, and it is very effective for the application in the evaluation of the cylindricity error.

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.

Fractional order fuzzy PID controller for wind energy‐based hybrid power system using quasi‐oppositional harmony search algorithm

In this study, a novel fuzzy logic control scheme is investigated for the frequency and power control of an isolated hybrid power system (HPS) (IHPS) and HPS-based two-area power system. The studied IHPS comprises of various autonomous generation systems such as wind energy, diesel engine generator and an energy storage device (i.e. capacitor energy storage device). A novel fractional order (FO) fuzzy proportional-integral-derivative (PID) (FO-F-PID) controller scheme is deployed and the various tunable parameters of the studied model are tuned by quasi-oppositional harmony search (HS) (QOHS) algorithm for improved performance. The QOHS algorithm (based on standard HS algorithm) accelerates the convergence speed by combining the concept of quasi-opposition in the basic HS framework. This FO-F-PID controller shows better performance over the integer order-PID and F-PID controller in both linear and non-linear operating regimes. The proposed FO-F-PID controller also shows stronger robustness properties against loading mismatch and different rate constraint non-linarites than other controller structures.

Harmony search based memetic algorithms for solving sudoku

The development of hybrid procedures for optimization focuses on enhancing the strength and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of the competing methodologies to create a superior solution procedure. The objective of this paper is to explore the hybridization between Harmony Search and Hill Climbing algorithm by utilizing the exploration power of the former and exploitation power of the latter in the context of solving Sudoku which is a well-known hard combinatorial optimization problem. We call this hybrid algorithm Harmony Search Hill Climber (HSHC). In order to extend the exploration capabilities of HSHC it is further modified to create three different algorithms namely Retrievable Harmony Search Hill Climber (RHSHC), Global Best Retrievable Harmony Search Hill Climber (GB-RHSHC) and Random Best Retrievable Harmony Search Hill Climber (RB-RHSHC). Comparing the four algorithms proposed in this paper RHSHC outperforms its three variations in terms of effectiveness. Experimental results demonstrate that RHSHC perform significantly better than standard Harmony Search algorithm and standard Hill climber algorithm. On comparing RHSHC with the genetic algorithm it has been concluded that former outperforms latter both in terms of effectiveness and efficiency particularly for Hard and Expert level puzzles. Comparing RHSHC and hybrid AC3-tabu search algorithm it has been concluded that RHSHC is very competent to hybrid AC3-tabu search algorithm.

An enhanced harmony search based algorithm for feature selection: Applications in epileptic seizure detection and prediction

Feature selection is a well-studied problem in the areas of pattern recognition and artificial intelligence. Apart from reducing computational cost and time, a good feature subset is also imperative in improving the classification accuracy of automated classifiers. In this work, a wrapper-based feature selection approach is proposed using the evolutionary harmony search algorithm, whereas the classifiers used are the wavelet neural networks. The metaheuristic algorithm is able to find near-optimal solutions within a reasonable amount of iterations. The modifications are accomplished in two ways—initialization of harmony memory and improvisation of solutions. The proposed algorithm is tested and verified using UCI benchmark data sets, as well as two real life binary classification problems, namely epileptic seizure detection and prediction. The simulation results show that the standard harmony search algorithm and other similar metaheuristic algorithms give comparable performance. In addition, the enhanced harmony search algorithm outperforms the standard harmony search algorithm.

An Analysis of the Parameter Modifications in Varieties of Harmony Search Algorithm

A Harmony Search (HS) algorithm is a population based-meta-heuristics approach that is superior in solving diversified and large scale optimization problems. Several studies have pointed out that Harmony Search is an efficient and flexible tool to resolve optimization problems in diverse areas of construction, engineering, robotics, telecommunication, health and energy. Considering its increasing usage in diverse areas, this paper aims to present the historical development of HS, highlighting on its different features, modifications for improvements and limitations. Based on the description of the fundamental concept of HS, recent variations of the extended HS were analyzed focusing on its algorithm’s theory as well as its fundamental and primary concepts. It was found that the enhancements made on the extended HS are mainly on the modification of the parameters, such as the harmony memory consideration rate (HMCR), pitch adjusting rate (PAR) and distance bandwidth (BW). This analysis provides a useful motivation for researchers interested to improve the achievement of the standard HS algorithm and enhance the solution convergence rate and flexibility.

An improved adaptive binary Harmony Search algorithm

Harmony Search (HS), inspired by the music improvisation process, is a new meta-heuristic optimization method and has been successfully used to tackle the optimization problems in discrete or continuous space. Although the standard HS algorithm is able to solve binary-coded optimization problems, the pitch adjustment operator of HS is degenerated in the binary space, which spoils the performance of the algorithm. Based on the analysis of the drawback of the standard HS, an improved adaptive binary Harmony Search (ABHS) algorithm is proposed in this paper to solve the binary-coded problems more effectively. Various adaptive mechanisms are examined and investigated, and a scalable adaptive strategy is developed for ABHS to enhance its search ability and robustness. The experimental results on the benchmark functions and 0–1 knapsack problems demonstrate that the proposed ABHS is efficient and effective, which outperforms the binary Harmony Search, the novel global Harmony Search algorithm and the discrete binary Particle Swarm Optimization in terms of the search accuracy and convergence speed.