Parameter-setting-free harmony search algorithm

Geem, Zong Woo; Sim, Kwee-Bo

doi:10.1016/j.amc.2010.09.049

Cited by 167 publications

(67 citation statements)

References 17 publications

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“…A ŷ New is updated by the heuristic operations of HS algorithm. Details are described in the Table below [9,13]. Also, Fig.…”

Section: Harmony Search Algorithm With Parametersetting Free Techniquementioning

confidence: 99%

“…Other meta-heuristic algorithms, such as GA, simulated annealing and particle swarm optimization, have a major disadvantage in that they require a considerable technique for setting of the initial model parameters. In order to solve this problem, Geem proposed the HS algorithm with respect to the parameter-setting-free technique [13]. The basic HS algorithm tries to find an optimal solution vectors that satisfies an objective function related with given problems.…”

Section: Harmony Search Algorithm With Parametersetting Free Techniquementioning

confidence: 99%

“…The initial parameter set consisting of HMMs was not considered by HS algorithm, because of the new parameter-setting-free technique that contains three operations (random selection, memory consideration, pitch adjustment) for every variable in harmony memory (HM). Also, HSA has the advantage of being free from divergence of solutions and local optima [13].…”

Section: Fig 3 Harmony Representation Of Hmm Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm

Ko¹,

Park²,

Park³

et al. 2012

Journal of Electrical Engineering and Technology

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-In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

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“…A ŷ New is updated by the heuristic operations of HS algorithm. Details are described in the Table below [9,13]. Also, Fig.…”

Section: Harmony Search Algorithm With Parametersetting Free Techniquementioning

confidence: 99%

Section: Harmony Search Algorithm With Parametersetting Free Techniquementioning

confidence: 99%

Section: Fig 3 Harmony Representation Of Hmm Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm

Ko¹,

Park²,

Park³

et al. 2012

Journal of Electrical Engineering and Technology

Self Cite

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show abstract

“…Additionally, the cumulative value of the derivatives of all candidate discrete values for each variable i x should be equal to unity: (14) In addition to this stochastic derivative, HS has a parameter-setting-free (PSF) process, which gives algorithm users user-friendliness without requiring tedious and time-consuming task of assigning proper values for algorithm parameters [36][37][38].…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search

Geem

2015

Water

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Thus far, various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping, ant colony optimization, harmony search, cross entropy, scatter search, and honey-bee mating, have been proposed to optimally design the water distribution networks with respect to design cost. However, flow velocity constraint, which is critical for structural robustness against water hammer or flow circulation against substance sedimentation, was seldom considered in the optimization formulation because of computational complexity. Thus, this study proposes a novel fuzzy-based velocity reliability index, which is to be maximized while the design cost is simultaneously minimized. The velocity reliability index is included in the existing cost optimization formulation and this extended multiobjective formulation is applied to two bench-mark problems. Results show that the model successfully found a Pareto set of multiobjective design solutions in terms of cost minimization and reliability maximization.

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“…It is hybridized with other successful optimization techniques such as GA (Nadi et al 2010), particle swarm optimization (PSO) (Omran and Mahdavi, 2008) and Hill climbing (Al-Betar et al 2012b). Furthermore, its parameter is deterministically adaptive during the search (Mahdavi et al 2007;Geem and Sim, 2010;Pan et al, 2010;Alatas, 2010). Quite recently, there have been some mathematical analysis studies to investigate the exploratory power of HSA (Das et al 2011;Al-Betar et al 2012a).…”

Section: Introductionmentioning

confidence: 99%

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

Abual-Rub

Al-Betar

Abdullah

et al. 2012

Netw Model Anal Health Inform Bioinforma

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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.

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Parameter-setting-free harmony search algorithm

Cited by 167 publications

References 17 publications

Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm

Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm

Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search

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

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