Self-adaptive Harmony Search Algorithm Research Articles

Mobile Robot Dynamic Path Planning Based on Self-Adaptive Harmony Search Algorithm and Morphin Algorithm

As a vital part of autonomous navigation of mobile robot, path planning is a hot research direction which aims at searching a shortest collision-free path from the starting position to the goal position in a complex environment. In this paper, a method for global dynamic path planning is designed based on improved self-adaptive harmony search algorithm (ISAHS) and Morphin algorithm. Firstly, to improve the quality of new solution vector, a neighbors and optimal learning strategy is introduced. Secondly, two key parameters are adjusted adaptively and a probability disturbance strategy is designed for renewing harmony memory, and then an improved self-adaptive harmony search algorithm is proposed to obtain an initial optimal path in the static environment. Finally, the Morphin algorithm is introduced to avoid the moving obstacles in real time. Simulation results indicate that the proposed method performs well in planning an initial static optimal path and it can avoid all preset moving obstacles effectively.

Fake news detection using an ensemble learning model based on Self-Adaptive Harmony Search algorithms

In general, the features of fake news are almost the same as those of real news, so it is not easy to identify them. In this paper, we propose a fake news detection system using a deep learning model. First, news articles are preprocessed and analyzed based on different training models. Then, an ensemble learning model combining four different models called embedding LSTM, depth LSTM, LIWC CNN, and N-gram CNN is proposed for fake news detection. Besides, to achieve higher accuracy in fake news detection, the optimized weights of the ensemble learning model are determined using the Self-Adaptive Harmony Search (SAHS) algorithm. In the experiments, we verify that the proposed model is superior to the state-of-the-art methods, with the highest accuracy of 99.4%. Furthermore, we also investigate the cross-domain intractability issue and achieve the highest accuracy of 72.3%. Finally, we believe there is still room for improving the ensemble learning model in addressing the cross-domain intractability issue.

An adaptive structure on a new local branching algorithm using instantaneous dimensions and convergence speed: a case study for multi-commodity network design problems

This study presents an adaptive parameter control structure for the New Local Branching Algorithm (NLBA) to enhance the overall efficiency of the algorithm by implementing an exploitation and exploration approach within solution search subspaces. In other words, the optimization problem is divided into several sub-problems and the proposed adaptive parameter control structure introduces some deterministic rules to check the convergence speed and adapt the performance of the algorithm in each subspace. So, the proposed adaptive structure uses an exploration approach to avoid becoming trapped at local optimum that may not be the global optimum, as well as an exploitation approach to look near good solutions for even better ones at each iteration. To evaluate the performance of the proposed adaptive parameter control structure, 26 multi-commodity network design problems are tested. The experimental results of this adaptive structure for NLBA (ANLBA) confirm that its performance compared to the original NLBA, a hybrid structure that consists of the Self-Adaptive Harmony Search (SAHS) algorithm as a parameter tuning method for NLBA, and CPLEX is significantly improved.

Information feedback self-adaptive harmony search algorithm for the bovine cortical bone vibration-assisted drilling optimization

In orthopedics and surgery, bone drilling is an important operation. Increasing demands call for bone drilling force prediction and minimization. Vibration-assisted drilling (VAD) provides an opportunity to reduce the force. In the study, the information feedback self-adaptive harmony search (IFSHS) algorithm is presented for the thrust force minimization in bovine cortical bone VAD. The IFSHS improves the drilling parameters which leads to the optimal cutting state. The dynamic searching parameters, optimization parameters evaluation, and evaluation information feedback are applied to search the optimal solution. This force model was developed and employed with IFSHS. The VAD drilling experiments and parameters analysis are carried out. Experimental results have shown that the VAD has the potential to reduce the thrust force. Compared with conventional drilling (CD), the thrust force decreased by 18.4%–33.2% under suitable VAD parameters. Compared with the results of the original VAD parameters, the optimized thrust force decreased by 20.19%.

Constrained Self-Adaptive Harmony Search Algorithm with 2-opt Swapping for Driver Scheduling Problem of University Shuttle Bus

A constrained self-adaptive harmony search algorithm (CSAHS) with 2-opt swapping (CSAHS-2opt) for driver scheduling problem of university shuttle bus (DSPUSB) was proposed in this study. In generating a schedule, there are some constraints that should be catered to obtain an optimum result. In DSPUSB, fairness of task distribution among drivers is one of the main issues that are very crucial and can affect the performance and quality of services. Therefore, to maximise the fairness, the violation of soft constraints for DSPUSB including shift and route should be minimised. CSAHS with 2-opt swapping was proposed focusing on minimising soft constraint violation. In standard harmony search (HS), the value of distance bandwidth (BW) parameter was static, while in this study the BW was dynamically changed and determined based on the current solution of each driver every week. Here, a set of distance BW value was formed based on shift constraint. In each iteration, the BW values were often changed and randomly chosen within the set, whereas the 2-opt swapping normally used in travelling salesman problem was applied for route constraint based on some rules. These improvements are capable of reducing the repetition task that leads to fairness issue. The result demonstrated that CSAHS-2opt gave better solutions compared with standard HS, improved HS and parameter-adaptive HS.

Relevant feature selection in the context of painting classification

In this paper, a painting genre classification system is proposed. Four feature descriptors about the color and texture defined in the MPEG-7 specification, which are more against painting characteristics, are extracted from data sets. Then, we use a self-adaptive harmony search algorithm to select relevant features (or a local feature set) to train each one-against-one SVM classifier. Finally, a majority voting strategy on N(N − 1)/2 prediction results would determine their respective genres of paintings. The experimental results show that the overall accuracy reaches 69.8%, and this demonstrates more precise features can be selected for each pair of genres to get better classification results. Besides, we also verify that if the number of painting genres is few, the classification method without feature selection will be sufficient to obtain good results. Finally, we propose new measurement to assess the methods with different datasets.

A multi-factor and high-order stock forecast model based on Type-2 FTS using cuckoo search and self-adaptive harmony search

With the developing economy, an increasing number of factors and high-order data have considerably influenced fluctuations in stock. The fuzzy time series (Type-1) model has been recently studied by many scholars. In this study, an enhanced model on the study of stock forecasting is proposed. This model is a multi-factor and high-order time series forecast model that the Type-2 fuzzy time series model by integrating several other factors. We first employ the cuckoo search algorithm instead of the conventional average method to partition the universe of discourse, and then propose a novel self-adaptive harmony search algorithm to optimize the high-order weight. Furthermore, the Shanghai Stock Exchange Composite Index and Taiwan Stock Exchange Capitalization Weighted Stock Index are used to verify the better performance of the proposed method. Experimental results show that the proposed method outperforms other baseline methods.

An intelligent method to reduce losses in distribution networks using self-adaptive harmony search algorithm

Loss reduction planning is considered one of the most significant and sensitive distribution designing system. The planning process to reduce losses along with distributed generation sources results in a non-convex, non-linear optimization problem, mixed with integer variables, which requires complicated calculations. To solve this problem, novel optimization algorithms such as harmony Search (HS) algorithm can be used for different optimization problems. This paper presents an effective intelligent optimization method to solve the network reconfiguration problem in the presence of distributed generation (DG) with an objective of minimizing real power loss and improving voltage profile in distribution system using new set of harmony search algorithms, called self-adaptive. To verify the efficiency of the algorithm the results have been analyzed in the IEEE standard system. The results show the strength, appropriate efficiency and superior performance of this algorithm in comparison to the original harmony Search algorithm and other optimization algorithms as well.

Music genre classification based on local feature selection using a self-adaptive harmony search algorithm

This paper proposes an automatic music genre-classification system based on a local feature-selection strategy by using a self-adaptive harmony search (SAHS) algorithm. First, five acoustic characteristics (i.e., intensity, pitch, timbre, tonality, and rhythm) are extracted to generate an original feature set. A feature-selection model using the SAHS algorithm is then employed for each pair of genres, thereby deriving the corresponding local feature set. Finally, each one-against-one support vector machine (SVM) classifier is fed with the corresponding local feature set, and the majority voting method is used to classify each musical recording. Experiments on the GTZAN dataset were conducted, demonstrating that our method is effective. The results show that the local-selection strategies using wrapper and filter approaches ranked first and third in performance among all relevant methods.

A Self-Adaptive Harmony Search Algorithm for Unconstrained Optimization Problems

Recently, a new meta-heuristic optimization algorithm–harmony search (HS) was developed, which imitates the behaviors of music improvisation. 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, a self-adaptive harmony search algorithm (SaHS) proposed. In this algorithm, we design a new parameter setting strategy to directly tune the parameters in the search process, and balance the process of exploitation and exploration. Finally, we use SaHS to solve unconstrained optimization problems so as to profoundly study and analyze the performance of the SaHS. The results show that the SaHS has better convergence accuracy than the other three harmony search algorithms.

Optimal capacitor placement in radial distribution systems using clustering based optimization

A Clustering Based Optimization (CBO) for the discrete optimisation problem of fixed shunt capacitor placement and sizing is presented. We minimize the sum of costs for power/energy losses and capacitor costs. Over-compensation and voltage constraints are also taken into consideration. CBO is based on a simple search which iteratively loops through network buses and places capacitors at locations that yield maximum reduction of losses in the objective function.The effectiveness of the proposed approach is demonstrated on a 22-bus, 34-bus, 69-bus and 85-bus distribution systems. The CBO results are better than the results from other methods from recent papers which include: Fuzzy Genetic Algorithm (FGA), Direct Search Algorithm (DSA), Teaching Learning Based Optimization (TLBO), Cuckoo Search (CS), Self Adaptive Harmony Search Algorithm (SAHSA) and Artificial Bee Colony (ABC). In addition, for all cases, the proposed method gives repetitive and unique results in significantly shorter computation time.

Solving economic load dispatch problems using chaotic self-adaptive differential harmony search algorithm

Summary In this paper, two improved differential harmony search algorithms called self-adaptive differential harmony search (SADHS) algorithm and chaotic SADHS (CSADHS) algorithm are proposed to solve economic load dispatch problems with valve-point effect, ramp-rate limits, prohibited operating zones, and multiple fuel options. The effectiveness and suitability of the proposed algorithms are demonstrated on test systems consisting of 13, 15, and 40 generating units with valve-point effect and 10 generating units with multiple fuel options and valve-point effect. The simulation results obtained by the proposed algorithms are compared with the results obtained using DHS and with the results of other methods reported in the literature. The solution obtained by the proposed CSADHS algorithm is found to be better than the other methods in terms of solution quality. In terms of convergence speed and computation time, the proposed CSADHS algorithm is better than DHS and the other proposed SADHS algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Solving Optimal Power Flow Problems Using Chaotic Self-adaptive Differential Harmony Search Algorithm

Optimal power flow is the basic tool that allows an electric utility to determine the economic and secure operating conditions of an electric power system. This article presents a chaotic self-adaptive differential harmony search algorithm to solve optimal power flow problems with non-smooth and non-convex cost functions. The searching capacity of the proposed chaotic self-adaptive differential harmony search algorithm has been improved by introducing a chaotic self-adaptive differential mutation operator instead of a pitch adjustment operator in the harmony search algorithm. The effectiveness of the proposed chaotic self-adaptive differential harmony search algorithm has been tested with IEEE 30-bus, IEEE 300-bus, and 66-bus Indian utility systems. The simulation results obtained using the proposed algorithm are compared with other variants of the improved harmony search algorithm, such as the differential harmony search algorithm, the chaotic differential harmony search algorithm, the interior point method, and other techniques reported in the literature, to show its effectiveness. The results obtained by the proposed algorithm are found to be better than the results obtained by the differential harmony search, chaotic differential harmony search, interior point method, and other algorithms reported in the literature in terms of solution quality and standard deviation of generation cost. In terms of speed of convergence and computational time, the proposed algorithm is better than the differential harmony search and chaotic differential harmony search algorithms.

A dynamic self-adaptive harmony search algorithm for continuous optimization problems

In solving global optimization problems for continuous functions, researchers often rely on metaheuristic algorithms to overcome the computational drawbacks of the existing numerical methods. A metaheuristic is an evolutionary algorithm that does not require the functions in the problem to satisfy specific conditions or mathematical properties. A recently proposed metaheuristic is the harmony search algorithm, which was inspired by the music improvisation process and has been applied successfully in the solution of various global optimization problems. However, the overall performance of this algorithm and its convergence properties are quite sensitive to the initial parameter settings. Several improvements of the harmony search algorithm have been proposed to incorporate self-adaptive features. In these modified versions of the algorithm, the parameters are automatically tuned during the optimization process to achieve superior results. This paper proposes a new dynamic and self-adaptive harmony search algorithm in which two of the optimization parameters, the pitch adjustment rate and the bandwidth, are auto-tuned. These two parameters have substantial influence on the quality of the final solution. The proposed algorithm utilizes two new quality measures to dynamically drive the optimization process: the current best-to-worst ratio of the harmony memory fitness function and the improvisation acceptance rate. The key difference between the proposed algorithm and most competing methods is that the values of the pitch adjustment rate and bandwidth are determined independently of the current improvisation count and therefore vary dynamically rather than monotonically. The results demonstrate the superiority of the proposed algorithm over various other recent methods based on several common benchmarking functions.

A Novel Self-Adaptive Harmony Search Algorithm

The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self-adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance. The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS), improved harmony search (IHS), and other self-adaptive variants for large-scale optimization problems and constrained problems.

Self-adaptive harmony search algorithm for optimization

Recently, a new meta-heuristic optimization algorithm – harmony search (HS) with continuous design variables was developed. This algorithm 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 used the consciousness (i.e., harmony memory) to automatically adjust parameter values. In addition, the pseudo-random number generator is also replaced by the low-discrepancy sequences for initialization of the harmony memory. Finally, the experimental results revealed the superiority of the proposed method to the original HS and recently developed variants.