Improved Cuckoo Search Algorithm for Feed forward Neural Network Training

Valian, Ehsan; Mohanna, Shahram; Tavakoli, Saeed

doi:10.5121/ijaia.2011.2304

Cited by 201 publications

(128 citation statements)

References 27 publications

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“…After that, these values should be decreased in final generations to result in a better fine-tuning of solution vectors. In Equations 12-14, where NI and g n are the number of total iterations and the current iteration, respectively [16]:…”

Section: Proposed Algorithm (Pso-cs) Classifiermentioning

confidence: 99%

Training Recurrent Neural Networks by a Hybrid PSO-Cuckoo Search Algorithm for Problems Optimization

Ibrahim¹,

Mohammed²

2017

IJCA

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Because of computational drawbacks of conventional numerical methods in solving complex optimization problems, researchers may have to rely on meta-heuristic algorithms. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. Also, the cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems .Normally, the parameters of the cuckoo search are kept constant. This may make algorithm suffering from slow convergence rate. To overcome with this issue, a hybrid algorithm called (PSO-CS classifier)for adjusting the cuckoo search parameters is presented to improved cuckoo search algorithm by particle swarm optimization (PSO) for training recurrent neural network which its weights and bias trained using the (PSO-CS classifier) to deviate from being stuck in local minima) for two benchmark classification problems. Moreover, to combine the ability of social communication in PSO with the local search capability of CS .Finally, the performance of the proposed algorithm is compared with that of the standard cuckoo search and PSO Algorithms. The simulation results show that the proposed (PSO-CS classifier) algorithm performs better than other algorithms in decrease number of training errors with a fast convergence rate and high accuracy. General TermsPSO,Cuckoo search algorithm,Classification.

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Section: Proposed Algorithm (Pso-cs) Classifiermentioning

confidence: 99%

Training Recurrent Neural Networks by a Hybrid PSO-Cuckoo Search Algorithm for Problems Optimization

Ibrahim¹,

Mohammed²

2017

IJCA

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“…Applications of cuckoo search includes for selecting optimal matching parameters in milling operations (92), cuckoo search involves in feed forward neural network training (93), for structural design optimization of vehicle components (94), for solving travelling sales man problem (95), the basic cuckoo search algorithm has been modified and utilized for unconstrained optimization problems (96), for satellite image segmentation in multi-level thresholding (97), the cuckoo search algorithm have been improved for global optimization (98), the hybridized cuckoo with fuzzy for solving multi-objective scheduling problem (99), parameter estimation for chaotic systems using cuckoo search algorithm (100).…”

Section: B Applications Areas Of Cuckoo Seach Optimizationmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.

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“…As a kind of global searching method of intelligent optimization, the Cuckoo Search has the advantages of easy to implement, less parameter setting and so on. Studies show that the CS algorithm is superior to the GA algorithm and PSO algorithm [6,7]. Therefore, Cuckoo Search algorithm is combined with BP neural network to build a CSBP prediction model of gas emission, and the experimental data are used to validate the accuracy of the prediction results.…”

Section: Introductionmentioning

confidence: 99%

Gas Emission Prediction Model of Coal Mine Based on CSBP Algorithm

2016

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Abstract:In view of the nonlinear characteristics of gas emission in a coal working face, a prediction method is proposed based on cuckoo search algorithm optimized BP neural network (CSBP). In the CSBP algorithm, the cuckoo search is adopted to optimize weight and threshold parameters of BP network, and obtains the global optimal solutions. Furthermore, the twelve main affecting factors of the gas emission in the coal working face are taken as input vectors of CSBP algorithm, the gas emission is acted as output vector, and then the prediction model of BP neural network with optimal parameters is established. The results show that the CSBP algorithm has batter generalization ability and higher prediction accuracy, and can be utilized effectively in the prediction of coal mine gas emission.

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Improved Cuckoo Search Algorithm for Feed forward Neural Network Training

Abstract: ABSTRACT

Cited by 201 publications

References 27 publications

Training Recurrent Neural Networks by a Hybrid PSO-Cuckoo Search Algorithm for Problems Optimization

Training Recurrent Neural Networks by a Hybrid PSO-Cuckoo Search Algorithm for Problems Optimization

A Review of Bio Inspired Computing and its Applications

Gas Emission Prediction Model of Coal Mine Based on CSBP Algorithm

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