An Intelligent Tuned Harmony Search algorithm for optimisation

Yadav, Parikshit; Kumar, Rajesh; Panda, Sanjib Kumar; Chang, C.S.

doi:10.1016/j.ins.2011.12.035

Cited by 180 publications

(79 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• HS and six variants: Chaos HS (CHS) [3], Mahdavi HS (MHS) [21]; Global-best HS (GHS) [25], Selfadaptative GSH (SGHS) [33], Intelligent Tunned HS (ITHS) [41]; and Novel Global HS (NGHS) [4];…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Yadav et al [41] have introduced a new HS variant called Intelligent Tunned HS (ITHS), which still depends on parameters used in the original HS, i.e., it does not introduce new parameters as stated by some aforementioned HS-based approaches. Additionally, PAR is automatically computed in ITHS, instead of traditional HS, in which the user needs to input it.…”

Section: Evolutionary Optimization Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Swarm-based Descriptor Combination and its Application for Image Classification

Mansano¹,

Matsuoka²,

Abiuzzi³

et al. 2014

ELCVIA

View full text Add to dashboard Cite

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Evolutionary Optimization Backgroundmentioning

confidence: 99%

Swarm-based Descriptor Combination and its Application for Image Classification

Mansano¹,

Matsuoka²,

Abiuzzi³

et al. 2014

ELCVIA

View full text Add to dashboard Cite

show abstract

“…As a result, different instances require different implementations and configurations in designing the algorithms. There are, however, a group of problems represented with a common XML structure but associated with different constraints and objectives [26]. Some effort has also been made in the literature to construct relatively general frameworks [31,32].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The idea is adapted in the search process for solving optimization problems [19]. HSA can be categorized as a recent evolutionary algorithm and showed to be efficient in solving difficult optimization problems such as university course timetabling [20], vehicle routing [21], Sudoku Puzzle [22], and many others [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A harmony search algorithm for nurse rostering problems

Hadwan

Ayob

Sabar

et al. 2013

Information Sciences

View full text Add to dashboard Cite

ABSTRACT. Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The Nurse Rostering Problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature.

show abstract

“…When applied to numerical optimization problems, it tends to perform badly in local searching. Lots of improved HS algorithms have been presented to enhance the performance of the HS algorithm [23,25,26]. Inspired by quantum computing, a new variation of the HS algorithm called quantum harmony search algorithm (QHS) is proposed in this paper.…”

Section: Quantum Harmony Search (Qhs) Algorithmmentioning

confidence: 99%

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

Hong

Sun

2013

Energies

View full text Add to dashboard Cite

Abstract:The accurate forecasting of carbon dioxide (CO 2 ) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn't consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO 2 emission of the World top-5 CO 2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

show abstract

An Intelligent Tuned Harmony Search algorithm for optimisation

Cited by 180 publications

References 52 publications

Swarm-based Descriptor Combination and its Application for Image Classification

Swarm-based Descriptor Combination and its Application for Image Classification

A harmony search algorithm for nurse rostering problems

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

Contact Info

Product

Resources

About