Harmony Memory Consideration Rate Research Articles

A Modified Harmony Search Algorithm Solve Numerical Optimization Problems

This paper proposes a new effective MHS algorithm to solve numerical optimization problems. The MHS algorithm first adopt a novel self-studying strategy, which makes it easy balance the global search ability and local development ability, prevent the MHS algorithm trapped into local optimal value. besides, the harmony memory consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth distance (bw) is changed with function values dynamically, it can effectively improve the convergence speed and precision of the algorithm Based on five test functions , experiments results obtained by the MHS algorithm are better than those obtained using HS, IHS and NGHS algorithm in the literature.

A Self-Study Harmony Search Algorithm for Optimization Problems

A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS)

하모니 검색 알고리즘을 이용한 포트홀 발생 개수 예측 모형

Abstract : A bunch of asphalt roads have been damaged frequently in relation to the rapid climate change. To solve and prevent this type of problems, many nationalities in the world have performed various researches. In this regard, the objective of this study is to develop prediction model as to the number of potholes occurred in seoul. At the same time, we have utilized empirical and statistical approaches in order for us to identify factors which is affecting the actual occurrence. The predictive model was determinded by using BHS (Basic Harmony Search) algorithm. Prediction was based on the weather and traffic data as well as data occurrence data of porthole. To assess the influences which are PAR(Pitch Adjusting Rate) and HMCR(Harmony Memory Considering Rate), we determined suitability by changing the values. In the process of the determining a predictive model, the predictive model composed Training data (2011, 2012 and 2013yrs data). To determine the suitability of the model, we have utilized Testing Set (2009 and 2010 yrs data). The suitability of the basic prediction model has been from RMSE(Root Mean Squared Error), MAE(Mean Absolute Error) and Coefficient of determination.

A Modified Harmony Search Algorithm for Numerical Optimization Problems

This paper presents a modified harmony search (MHS) algorithm for solving numerical optimization problems. MHS employs a novel self-learning strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In the proposed MHS algorithm, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. The other two key parameters PAR and bw adjust dynamically with generation number. Based on a large number of experiments, MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and GHS).

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.

A novel adaptive modified harmony search algorithm to solve multi-objective environmental/economic dispatch

This paper proposes a novel adaptive modification approach based on harmony search algorithm (HS) to solve the multi- objective environmental economic dispatch problem. The proposed algorithm makes use of adaptive formulations to update the adjusting parameters of HS including the pitch adjusting and bandwidth parameters and the harmony memory consideration rate during the optimization process. Meanwhile, a useful modification is proposed to improve the variety of the harmony population effectively. In order to handle both the cost and emission objective functions, the ideas of trapezoidal fuzzy membership function and weighting factor are employed. The satisfying performance of the proposed method is examined through the IEEE standard test system.

Clustering using modified harmony search algorithm

Metaheuristic techniques are being successfully used as optimisation methods in various application areas. This paper presents modification in one such metaheuristic called as harmony search (HS) that is inspired from music improvisation process. The two parameters, harmony memory consideration rate (HMCR) and pitch adjusting rate (PAR), in HS play important role in improvisation of new harmony. Instead of keeping the parameters fixed, as reported in many existing algorithms, these are being allowed to change dynamically during the process of improvisation in the proposed algorithm. This paper further examines the effect on the results when K-means is initialised with solution returned by the proposed algorithm. The effect of harmony memory size has also been investigated on proposed approach. The experiments are performed for data clustering on nine benchmark datasets. The clustering performance of proposed algorithm is compared with K-means, fuzzy C-means, genetic algorithm, and four recently proposed variants of HS. The results are encouraging and demonstrate that the proposed algorithm provides much better values in terms of precision, recall, G-measure, inter-cluster and intra-cluster distances.

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

An Intelligent Tuned Harmony Search algorithm for optimisation

The Harmony Search (HS) algorithm is a population-based metaheuristic optimisation algorithm. This algorithm is inspired by the music improvisation process in which the musician searches for harmony and continues to polish the pitches to obtain a better harmony. Although several variants of the HS algorithm have been proposed, their effectiveness in dealing with diverse problems is still unsatisfactory. The performances of these variants mainly depend on the selection of different parameters of the algorithm. In this paper, a new variant of the HS algorithm is proposed that maintains a proper balance between diversification and intensification throughout the search process by automatically selecting the proper pitch adjustment strategy based on its Harmony Memory. However, the performance of the proposed Intelligent Tuned Harmony Search (ITHS) algorithm is influenced by other parameters, such as the Harmony Memory Size (HMS) and the Harmony Memory Considering Rate (HMCR). The effects that varying these parameters have on the performance of the ITHS algorithm is also analysed in detail. The performance of the proposed ITHS algorithm is investigated and compared with eight state-of-the-art HS variants over 17 benchmark functions. Furthermore, to investigate the robustness of the proposed algorithm at higher dimensions, a scalability study is also performed. Finally, the numerical results obtained reflect the superiority of the proposed ITHS algorithm in terms of accuracy, convergence speed, and robustness when compared with other state-of-the-art HS variants.

Effect of Harmony Search Parameters’ Variation in Clustering

Harmony search (HS) is a relatively new meta-heuristic optimization method, which is based on the concept of music improvisation. This paper depicts the impact of constant parameters such as Harmony Memory Consideration Rate and Pitch Adjusting Rate, and presents an approach for parameter tuning. It presents modifications in existing harmony search, by choosing appropriate values of these two parameters and allows them to change dynamically during the process of improvisation. The proposed algorithm has been evaluated for data clustering on five benchmark datasets. The clustering performance of proposed algorithm is compared with K-Means, Genetic algorithm, HS and improved version of HS. Experimental results reveal that proposed algorithm provides better results than the above said techniques in terms of precision, recall, G-Measure, inter-cluster and intra-cluster distance.

Dynamic Adaptive Harmony Search Algorithm for Optimization

A new meta-heuristic optimization algorithm–harmony search is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Although several variants and an increasing number of applications have appeared, one of its main difficulties is how to select suitable parameter values. In this paper, we proposed a novel algorithm to dynamically adapt the harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) and distance bandwidth (BW). The experimental results revealed the superiority of the proposed method to the original HS, improved harmony search (IHS) and global-best harmony search (GHS).

A self-adaptive global best harmony search algorithm for continuous optimization problems

This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate ( HMCR) and pitch adjustment rate ( PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth ( BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants.