Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the...
详细信息
ISBN:
(纸本)9781479981144
Data clustering is one of widely used methods for data mining. The k-means approach is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. But some hindrances such as the sensitivity to initial values and cluster centers or the risk of trapping in local optimal reduce its best performance. The purpose of kmeans method is minimizing the dissimilarity of observations, from cluster centers. In this paper, a new solution method inspired by harmonysearch combined with bee algorithm is introduced to improve performance k-means clustering. In this study, harmony and clustering structures are combined to produce harmony clustering. To avoid initial random selection, seed cluster center is considered in primary population as well as bee algorithm has been employed to increase the efficiency of algorithm. The proposed methods have been tested on standard benchmark data sets and also compared to other methods in the literature;it is noted that results show a promising performance leading to better efficiency and capability of the proposed solution.
Considering the phenomenon that harmony search algorithm sometimes abandon the optimal solution in finetuning, this text extracted the thoughts of cooperative coevolution from the coevolution theory, and advanced the ...
详细信息
ISBN:
(纸本)9781479953721
Considering the phenomenon that harmony search algorithm sometimes abandon the optimal solution in finetuning, this text extracted the thoughts of cooperative coevolution from the coevolution theory, and advanced the cooperation based on harmony Memory and Collaboration harmony search algorithm (CHS), then apply it in path planning of underwater penetration. No matter in Calculation precision or in astringent accuracy, the improved CHS algorithms is better than the unmodified harmony search algorithm (HS). Simulation shows the improved algorithm proposed in this paper performs excellently, and can be applied to the underwater penetration path planning problems.
This paper proposes the application of a novel meta-heuristic algorithm to the metropolitan wireless local area network deployment problem. In this problem, the coverage level of the deployed network must be maximized...
详细信息
This paper proposes the application of a novel meta-heuristic algorithm to the metropolitan wireless local area network deployment problem. In this problem, the coverage level of the deployed network must be maximized while meeting an assigned maximum budget, set beforehand. Specifically, we propose an approach based on the harmonysearch (HS) algorithm, with three main technical contributions: (1) the adaptation of the HS algorithm to a grouping scheme;(2) the adaptation of the improvisation operators driving the algorithm to the specific characteristics of the optimization problem to be tackled;and (3) its performance assessment via a simulated experiment inspired by real statistics in the city of Bilbao (Basque Country, northern Spain). Moreover, a comparison study of the proposed algorithm with a previous published grouping genetic algorithm is carried out, to further validate its performance. In light of the simulation results obtained from extensive experiments and several complexity considerations, we conclude that the proposed algorithm outperforms its genetically inspired counterpart, not only in terms of computation time, but also in the coverage level of the solution obtained. (c) 2012 Elsevier Ltd. All rights reserved.
A new weighted-sum multiobjective approach is investigated for order reduction based on Routh-Pade approximation, in which the harmony search algorithm is used to optimize the reduced-order model's parameters. In ...
详细信息
A new weighted-sum multiobjective approach is investigated for order reduction based on Routh-Pade approximation, in which the harmony search algorithm is used to optimize the reduced-order model's parameters. In this method, apart from minimizing the errors between a set of subsequent time moments/Markov parameters of the system and those of the model, the error between the singular values of the reduced-order system and those of the original system is minimized The Routh criterion is applied for specifying the stability conditions. The stability condition is then considered as a constraint in the optimization problem. To present the ability of the proposed method, 3 test systems are reduced. The results obtained show that the proposed approach performs well.
This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the dev...
详细信息
This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.
In this paper a new approach using harmonysearch (HS) algorithm is presented for placing Distributed Generators (DGs) in radial distribution systems. The approach is making use of a multiple objective planning framew...
详细信息
In this paper a new approach using harmonysearch (HS) algorithm is presented for placing Distributed Generators (DGs) in radial distribution systems. The approach is making use of a multiple objective planning framework, named an Improved Multi-objective HS (IMOHS), to evaluate the impact of DG placement and sizing for an optimal development of the distribution system. In this study, the optimum sizes and locations of DG units are found by considering the power losses and voltage profile as objective functions. The feasibility of the proposed technique is demonstrated in two distribution networks, where the qualitative comparisons are made against a well-known technique, known as Non-dominated Sorting Genetic algorithm II (NSGA-II). Furthermore, the results obtained are compared with those available in the literature.
A new method for model reduction of linear systems is presented, based on Chebyshev rational functions, using the harmonysearch (HS) algorithm. First, the full order system is expanded and then a set of parameters in...
详细信息
A new method for model reduction of linear systems is presented, based on Chebyshev rational functions, using the harmonysearch (HS) algorithm. First, the full order system is expanded and then a set of parameters in a fixed structure are determined, whose values define the reduced order system. The values are obtained by minimizing the errors between the I first coefficients of the Chebyshev rational function expansion of full and reduced systems, using the HS algorithm. To assure stability, the Routh criterion is used as constraints in the optimization problem. To present the ability of the proposed method, three test systems are reduced. The results obtained are compared with other existing techniques. The results obtained show the accuracy and efficiency of the proposed method. (C) 2013 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
harmonysearch (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. In order to enhance the accuracy and convergence rate of harmonysearch, a hybrid harmonysearch is...
详细信息
harmonysearch (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. In order to enhance the accuracy and convergence rate of harmonysearch, a hybrid harmonysearch is proposed by incorporating the artificial bee colony algorithm (ABC). The artificial bee colony algorithm is a new swarm intelligence technique inspired by intelligent foraging behavior of honey bees. The ABC and its variants are used to improve harmony memory (HM). To compare and analyze the performance of our proposed hybrid algorithms, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the hybrid algorithms are discussed by a uniform design experiment. Numerical results show that the proposed algorithms can find better solutions when compared to HS and other heuristic algorithms and are powerful searchalgorithms for various global optimization problems. (C) 2012 Elsevier Ltd. All rights reserved.
In this paper we proposed a harmonysearch-based Hyper-heuristic (HSHH) method for examination timetabling problems. The harmony search algorithm (HSA) is a relatively new metaheuristic algorithm inspired by the music...
详细信息
ISBN:
(纸本)9781467356091;9781467356084
In this paper we proposed a harmonysearch-based Hyper-heuristic (HSHH) method for examination timetabling problems. The harmony search algorithm (HSA) is a relatively new metaheuristic algorithm inspired by the musical improvisation process. The Hyper-heuristic is a new trend in optimization that uses a high level heuristic selected from a set of low-level heuristic methods. Examination timetabling is a combinatorial optimization problem which belongs to NP-hard class in almost all of its variations. In HSHH approach, the HSA will operate at a high level of abstraction which intelligently evolves a sequence of improvement low-level heuristics to use for examination timetabling problem. Each low-level heuristics represents a move and swap strategies. We test the proposed method using ITC-2007 benchmark datasets that has 12 de facto datasets of different complexity and size. The proposed method produced competitively comparable results.
harmonysearch based optimum design method is presented for the grillage systems. This numerical technique imitates the musical performance process that takes place when a musician searches for a better state of harmo...
详细信息
harmonysearch based optimum design method is presented for the grillage systems. This numerical technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the serviceability and ultimate strength constraints which are implemented from Load and Resistance Factor Design-American Institute of Steel Construction (LRFD-AISC). It selects the appropriate W-sections for the transverse and longitudinal beams of the grillage system out of 272 discrete W-section designations given in LRFD-AISC. This selection is carried out such that the design limitations described in LRFD-AISC are satisfied and the weight of the system is the minimum. Many design examples are considered to demonstrate the efficiency of the algorithm presented.
暂无评论