Improved harmony search algorithm with perturbation strategy

This paper proposes an improved harmony search (HS) algorithm for the solution of the static transmission expansion planning (TEP) problem with security constraints. The modification of this method in comparison to the original harmony search algorithm includes the introduction of a variable bandwidth in the memory consideration and the pitch adjustment phase for the selection of the new harmony vector (NHV) in the improvisation step of the algorithm. In that way, if previous harmonies are considered for the NHV, the algorithm allows only the best harmonies of the harmony memory in respect of objective function value to participate in the improvisation of the NHV. The results from the application of the proposed method on IEEE 24-bus reliability test system demonstrate the potentials and the practicality of the proposed HS algorithm. (6 pages)

In this paper a new method for dynamic parameter adaptation in harmony search (HS) algorithm is proposed. HS is a music inspired metaheuristic optimization algorithm, in particular we refer to jazz improvisation and is applied to solve complex problems. In this paper we propose an improvement to the convergence of the harmony search algorithm using fuzzy logic. Simulation results show that the propose approach improves the performance of HS, and benchmark mathematical functions are used to illustrate the feasibility of the proposed approach.

Inspired by the search method of harmony search algorithms, we propose an other version of improved harmony search(IHS)algorithm to solve integer programming problems. This paper designs a novel improvising strategy which is beneficial to the convergence of algorithm in the early stage of iteration and increasing the diversity of population in the late stage of iteration. Experimental results show that the proposed algorithm can be an efficient alternative for solving integer programming problems.

Harmony search (HS) algorithm is an emerging population oriented stochastic metaheuristic algorithm, which is inspired by the music improvisation process. This paper introduces an Amended harmony search (AHS) algorithm for solving optimization problems. In AHS, an enhanced approach is employed for generating better solution that improves accuracy and convergence speed of harmony search (HS). The effects of various parameters on harmony search algorithm are analyzed in this paper. The proposed approach performs fine tuning of two parameters bandwidth and pitch adjustment rate. The proposed algorithm is demonstrated on complex benchmark functions and results are compared with two recent variants of HS optimization algorithms, improved harmony search (IHS) and highly reliable harmony search (HRHS). Results suggested that the AHS method has strong convergence and has better balance capacity of exploration and exploitation.

An improved harmony search algorithm for power economic load dispatch

Harmony Search (HS) algorithm is a new meta-heuristic global search algorithm. To solve the algorithm easily fall into the local optimal problem, this paper proposes an adaptive novel global harmony search (ANGHS) algorithm based on NGHS algorithm. First, a new location update strategy is given using the adjustment parameter F. Then, the mutation operation is introduced in the case of excluding the small probability Pm. In order to avoid blind adjustment of the step size in the late search and effectively adjust the structure of the solution, the definition of the norm is introduced to adjust parameter bw dynamically. In this paper, 7 standard Benchmark functions are used to simulate the experiment. The results show that the algorithm avoids the premature problem of the algorithm and enhances the global search ability of the algorithm, with obvious improvement in convergence speed and stability, and has good optimize performance.

A method based on harmony search (HS) algorithm for designing an optical comb generator with excellent flatness is proposed. The comb generator with simple structure consists of four phase modulators with a single laser as the input, single radio frequency (RF) bias with different amplitudes, and DC bias for each modulator. No RF phase shifter, filter, or fiber loop is needed. Instead of following a classic mathematical model for finding the values of RF amplitudes and DC voltages for generating an optical comb, HS algorithm is implemented, which is an intelligent algorithm inspired from the improvisation of music players and seeks for the most harmonized answers corresponding to flat optical combs. The simulation results of generating two sets of optical combs by HS algorithm with 43 and 53 lines with flatness of 0.51 and 0.59 dB and then after inclusion of power conversion efficiency in the objective function of HS algorithm, two other sets of combs with 47 and 55 lines, flatness of 1.64 and 1.77 dB, and power conversion efficiency of 28% and 32% are presented. Such a device can be used in wavelength-division multiplexing systems and many other applications.

This work presents the development of multi-objective harmony search (MOHS) algorithm for optimization problem using self-adaptive improved harmony search (SIHS) algorithm which is a variant of recently developed harmony search algorithm (HS) for single objective optimization. The approach used in this work is decomposing of multiple objectives into several single objective functions which are simultaneously optimized in such a way that a nearly uniform distribution of the solutions along the pareto-front is followed. The algorithm upon testing for standard test functions has shown promising results.KeywordsHarmony SearchMulti-objective optimizationPareto front

Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output.The Harmony Search (HS) algorithm is based on the improvisation process of music.In comparison to other algorithms, the HSA has gain popularity and superiority in the field of evolutionary computation.When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization can be done by adjusting the input pitches and generate the perfect harmony.The test case generation process is used to identify test cases with resources and also identifies critical domain requirements.In this paper, the role of Harmony search meta-heuristic search technique is analyzed in generating random test data and optimized those test data.Test data are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM through Harmony search.It is observed that this algorithm generates suitable test cases as well as test data and gives brief details about the Harmony search method.It is used for test data generation and optimization.

This paper propose a harmony search (HS) algorithm to minimize makespan for the blocking flow shop scheduling problem. In the proposed algorithm, the HS-based search is employed to evolve harmony vectors to perform exploration, whereas a local search procedure based on the insert neighborhood is used to enhance the local exploitation ability. Extensive computational experiments and comparisons are carried out. Computational results show that the proposed HS algorithm is effective in finding optimal and near-optimal solutions.

Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat’s ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

Harmony search (HS), mimicking the musician's improvisation behavior, has demonstrated strong efficacy in optimization. To deal with the deficiencies in the original HS, a dynamic regional harmony search (DRHS) algorithm with opposition and local learning is proposed. DRHS utilizes opposition-based initialization, and performs independent harmony searches with respect to multiple groups created by periodically regrouping the harmony memory. An opposition-based harmony creation scheme is used in DRHS to update each group memory. Any prematurely converged group is restarted with its size being doubled to enhance exploration. Local search is periodically applied to exploit promising regions around top-ranked candidate solutions. DRHS consistently outperforms HS on 12 numerical test problems from the CEC2005 benchmark at both 10D and 30D.

Shen, G., 2020. Research on marine water quality evaluation model based on improved harmony search algorithm by Gaussian disturbance to optimize Takagi-Sugeno fuzzy neural network. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 283–287. Coconut Creek (Florida), ISSN 0749-0208.Marine water quality is affected by many factors and is difficult to evaluate accurately. An improved harmony search algorithm based on Gaussian disturbance (GDHS) is proposed to optimize the Takagi-Sugeno (T-S) fuzzy neural network (TSFNN) model of marine water quality evaluation. First, the basic harmony search algorithm is improved by the Gaussian disturbance global optimal harmony strategy and dynamic adjustment parameters. Second, the parameters of the T-S fuzzy neural network are optimized by GDHS to improve the stability and global convergence speed of the TSFNN. Finally, China's marine water quality standards and the actual conditions of the marine area to which the experimental data belong are considered, five indicators are selected to construct a neural network, and the water quality evaluation results of the data of 10 monitoring stations in the eastern waters of Qingdao show that the smaller the evaluation error of the GDHS-TSFNN model, the higher the accuracy.

liujie8114@163.com Abstract: An improved Harmony Search Algorithm applied to the Route Optimization has been proposed in this paper. At the same time, this paper compared this improved algorithm with the traditional Harmony Search Algorithm and the Genetic Algorithm through experiments and simulation. The experimental results show that the improved Harmony Search Algorithm has better performance in both convergence and stability in solving Route Optimization problem.

Implementation of harmony search on embedded platform