Classical Harmony Search Algorithm Research Articles

Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem

Abstract Harmony Search (HS) is a population based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony and has ability to escape from local minima, does not require differential gradients and initial value setting for the variables and free from divergence and has strong ability on exploring the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a memetic algorithm approach considering Harmony Search and Random Search is presented in the proposed research to solve unit commitment problem of electric power system. The proposed memetic algorithm is tested for IEEE benchmarks consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is tested for unimodal and multimodal benchmark functions and is compared with others well known evolutionary, heuristics and meta-heuristics search algorithms and it has been found that performance of proposed hybrid algorithm is much better than classical Harmony Search Algorithm and Improved Harmony Search Algorithm as well as recently developed algorithms.

Hybrid HS–random search algorithm considering ensemble and pitch violation for unit commitment problem

Harmony search (HS) is a population-based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony. The pitch of each musical instrument determines the aesthetic quality, just as the fitness function value determines the quality of decision variables. In the musical improvisation process, all players sound pitches within possible range together to make one harmony. If all the pitches make a good harmony, each player stores in his memory that experience and the possibility of making a good harmony is increased next time. Even though HS has the ability to escape from local minima, it does not require differential gradients and initial value setting for the variables, is free from divergence and has strong ability to explore the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a novel and hybrid version of harmony search combined with random search algorithm is presented in the proposed research to solve single-area unit commitment problem of electric power system. The proposed algorithm is tested for standard IEEE systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and metaheuristics search algorithms, and it has been found that performance of proposed algorithm is much better than of classical harmony search algorithm and improved harmony search algorithm as well as recently developed algorithms. Sensitivity analysis on proposed algorithm shows that low value of pitch adjustment rate results in better cost, and parametric test on proposed algorithm shows the rejection of the null hypothesis at the alpha significance level.

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.

Application of Improved Harmony Search Algorithm in Test Case Selection

Regressiontest is an effective means to ensure the quality of software. But the testefficiency will become lower and lower as the test case library becomesincreasingly large in the test. Therefore, in order to improve the efficiencyand quality of regression test, it is necessary to implement the test casesafety reduction. The optimization selection of the test case is an effectivemeans of the test case safety reduction. In order to solve the problem of testcases optimization selection, this paper proposes a test case selection methodbased on the improved harmony search algorithm. Specifically, the researcheradds an excellent harmony element library based on the classical harmony searchalgorithm for storing the searched excellent test case; meanwhile, he alsoimproves the way of generating the new harmony toallow these excellent test cases to be retained with a certain probability. Inthe search process, the researcher makes the adaptive adjustment of thealgorithm parameter BW in a non-linear incremental manner, improving the globalsearch capability of the algorithm. In addition, the excellence protectionstrategies are also increased to prevent against the degradation in theoptimization search process. In order to verify the feasibility of the improvedalgorithm, the researcher writes four programs: the genetic algorithm,differential evolution algorithm, classical harmony algorithm and improvedalgorithm, and conducted the comparative experiment. The experimental resultshows that the improved algorithm significantly improves the optimizationsearch performance compared with the classical harmony search algorithm and hasthe better performance compared with the genetic algorithm and differentialevolution algorithm.

A modified harmony search method for environmental/economic load dispatch of real-world power systems

Economic load dispatch (ELD) problem is one of the basic and important optimization problems in a power system. However, considering practical constraints of real-world power systems such as ramp rate limits, prohibited operating zones, valve loading effects, multi-fuel options, spinning reserve and transmission system losses in ELD problem makes it a non-convex optimization problem, which is a challenging one and cannot be solved by traditional methods. Moreover, considering environmental issues, results in combined economic and emission load dispatch (CEELD) problem that is a multiobjective optimization model with two non-commensurable and contradictory objectives. In this paper, a modified harmony search algorithm (MHSA) proposed and applied to solve ELD and CEELD problem considering the abovementioned constraints. In the proposed MHSA, a new improvising method based on wavelet mutation together with a new memory consideration scheme based on the roulette wheel mechanism are proposed which improves the accuracy, convergence speed, and robustness of the classical HSA. Performance of the proposed algorithm is investigated by applying it to solve various test systems having non-convex solution spaces. To Show the effectiveness of the proposed method, obtained results compared with classical harmony search algorithm (HSA) and some of the most recently published papers in the area.

Hysteresis parameters estimation using a modified harmony search

Purpose – The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model. Design/methodology/approach – In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated. Findings – The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model. Originality/value – This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.

Improved Harmony Search Algorithm for Solving Optimization Problem with Mixed Discrete Variables

The classical harmony search algorithm (HSA) can only be used to solve the unconstrained optimization problems with continuous decision variables. Therefore, the classical HSA is not suitable for solving an engineering optimization problem with mixed discrete variables. In order to improve the classical HSA, an engineering method for dealing with mixed discrete decision variables is introduced and an exact non-differentiable penalty function is used to transform the constrained optimization design model into an unconstrained mathematical model. Based on above improvements, a program of improved HSA is designed and it can be used for solving the constrained optimization design problems with continuous variables, integer variables and non-equidistant discrete variables. Finally, an optimization design example of single-stage cylindrical-gear reducer with mixed-discrete variables is given. The example shows that the designed program runs steadily and the proposed method is effective in engineering design.

A hybrid harmony search algorithm for ab initio protein tertiary structure prediction

Predicting the tertiary structure of proteins from their linear sequence is a big challenge in biology. The existing computational methods are not powerful enough to search for the precise structure in a huge conformational space. This inadequate capability of the computational methods, however, is a major obstacle when trying to tackle this problem. The observations of some previous studies have revealed much interest in hybridizing a local search-based metahuristic algorithm within the population-based metahuristic algorithm. This study introduces a hybrid harmony search algorithm (HHSA) as a means to solve ab initio protein tertiary structure prediction problem. In HHSA, the iterated local search (ILS) is incorporated with the harmony search algorithm (HSA) to empower it so as to find the local optimal solution within the search space of the new harmony. Furthermore, the global-best concept of particle swarm optimization (PSO) is incorporated in memory consideration as a selection scheme to accelerate the convergence speed. The HHSA predicts the tertiary structure of a protein giving its sequence alone (i.e., from scratch). Our algorithm converges faster than the classical harmony search algorithm. We evaluate our algorithm using two protein sequences. The results show that our algorithm can find more precise solutions than other previous studies.

Chaotic harmony search algorithms

Harmony Search (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. Like the use of chaos in adjusting note parameters such as pitch, dynamic, rhythm, duration, tempo, instrument selection, attack time, etc. in real music and in sound synthesis and timbre construction, this paper proposes new HS algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the HS to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical HS algorithm. Seven new chaotic HS algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of HS and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.