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

Pan, Quan-Ke; Suganthan, Ponnuthurai Nagaratnam; Taşgetiren, M. Fatih; Liang, Jing

doi:10.1016/j.amc.2010.01.088

Cited by 316 publications

(166 citation statements)

References 21 publications

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“…• 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%

“…Zou et al [44] proposed the Novel Global Harmony Search (NGHS), which differs from traditional HS with regard to three aspects: in the NGHS, the HMCR (Harmony Memory Considering Rate) and PAR parameters are excluded; a mutation probability p m is used; finally, NGHS has a modified improvisation scheme, and it always replaces the worst harmony with the new one. Finally, Pan [26] proposed the Self-adaptive Global best Harmony Search (SGHS) approach, which has a new improvisation scheme based on GHS. In addition, HMCR and PAR are modeled as self-adaptive parameters.…”

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

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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.

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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

“…Similarly, considering the efficiency of HS algorithm-that has a slow convergence rate, but guarantees a near-optimum solution [117]-many researchers applied HS for optimizing weight vector of the FNNs [175,176]. Moreover, the efficiency of HS comes from using m many harmonies (weight vectors), and iteratively improvising each harmony by computing new harmony (new solution vectors) using heuristic inspired by music pitch modification [117,177,178].…”

Section: Weight Optimizationmentioning

confidence: 99%

Metaheuristic design of feedforward neural networks: A review of two decades of research

Ojha

Abraham

Snel

2017

Engineering Applications of Artificial Intelligence

457

154

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Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

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“…Harmony Search (HS) is an evolutionary algorithm, which mimics musicians' behaviors, such as random play, memory-based play, and pitch-adjusted play when they perform. HS has proved to be a powerful tool for solving several optimization problems [22][23][24][25][26][27][28][29][30][31][32]. It does not need any mathematical calculations to obtain the optimal solutions.…”

Section: Introductionmentioning

confidence: 99%