Training Neural Networks with Krill Herd Algorithm

Kowalski, Piotr A.; źUkasik, Szymon

doi:10.1007/s11063-015-9463-0

Cited by 49 publications

(25 citation statements)

References 20 publications

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“…In addition, the proposed MMPSO algorithm is suitable for parallel implementation and the runtime of the MMPSO algorithm can be reduced to a much shorter time with parallel programming. Finally, the performance of the proposed MMPSO algorithm is compared with the performance reported in the literature for the HSA [16], KHA, GA [15], and the fireworks algorithm (FWA) [17] which split the data into 80% training and 20% testing, for six datasets. In order to make this comparison under the same conditions, six datasets are split into 80% training and 20% testing for this experiment.…”

Section: Resultsmentioning

confidence: 99%

“…Bolaji et al used the fireworks optimization algorithm (FWA) for ANN training and performed the experimental tests with UCI datasets. The experimental results were compared to the results obtained from the krill herd algorithm (KHA) [15], harmony search algorithm (HSA), and GA [16]. According to the experimental results, the FWA algorithm showed better classification performance [17].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Hacıbeyoğlu

Ibrahim

2018

Scientific Programming

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Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feedforward artificial neural networks training.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

Hacıbeyoğlu

Ibrahim

2018

Scientific Programming

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“…The KHA procedure has been verified for application within optimization problems in the case of discrete input data [27], while a parallel version of this procedure is put forward in [28]. Furthermore, it has been applied in medical tasks [29], for data base domains [30], in mechanism and machine theory [31], in clustering tasks [32], [33], and also in neural learning processes [34]. Extensive use of this algorithm has been collected in the article [35].…”

Section: Optimisation Based On Krill Herd Algorithmmentioning

confidence: 99%

“…This is a number in the range of (0.0; 1.0), and it represents the effect of the phase of the food search on the movement 34 PROCEEDINGS OF THE FEDCSIS. PRAGUE, 2017 Increasing the value of ω f , therefore, resulted in better player performance.…”

Section: Numerical Simulationsmentioning

confidence: 99%

On the Use of Nature Inspired Metaheuristic in Computer Game

Kowalski¹,

Łukasik²,

Charytanowicz³

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-This paper describes the use of a new swarm-based metaheuristic, namely Krill Herd Algorithm (KHA), in computer gaming. In this work, KHA is employed to find a bots movement strategy in a computer racing game. The complete algorithm is implemented using a Unity Engine in C# language. Herein, the triggering of the metaheuristic optimization task was conducted by the way of a KHA internal parameter investigation. In this approach, the goal of the race (the KHA evaluation function) for both the human and computer player is to finish a lap in the shortest time possible.

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“…This method is also used in a real life problem and showed good results. The Krill Herd algorithm is utilized for learning process of ANNs in [15]. In this work, the authors translated the positon of all weights and biases from ANNs into the vector.…”

mentioning

confidence: 99%

The Effect of Bat Algorithm and Genetic Algortihm on the Training Performance of Artificial Neural Networks

Hacıbeyoğlu¹,

Ibrahim²,

Alaykiran³

2017

International Journal of Research Studies in Computer Science a

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INTRODUCTIONArtificial Neural Networks (ANNs) which is a branch of artificial intelligence is one of the frequently used classification algorithm to solve data mining problems in real applications [1]. Training process has great importance for the success of ANNs and several learning algorithms are proposed by researchers in the literature. The Back-propagation learning algorithm is generally used for the training process of ANNs in the literature. But BP learning algorithm may fall into local minimum and converges slowly which is caused by the neuron saturation in the hidden layer [2]. Recently, optimization algorithms have been widely used for the learning process of ANNs instead of BP learning algorithm by researchers. Various optimization algorithms with a global search feature are used to improve the performance of ANNs from being stuck to local minima problem. In [3], tabu search algorithm is examined as an alternative to problematic backpropagation algorithm and when the results derived from seven test functions are investigated, it is found out that tabu search algorithm yielded significantly better results than backpropagation solutions. [4] investigated the performance of a variation of hill climbing algorithm on artificial neural network training and compared the results to the performance of simulated annealing and standard hill climbing algorithms. PSO algorithm that is adopted by [5] is one of the most important algorithms that have the interesting performance for training ANNs. Another study has been done by for training ANNs using Convexity Based Algorithm (CBA). The results of the study prove that the CBA fill in a critical gap in utilizing of the classification algorithms [6]. In [7], researchers adopted a hybrid approach that combine PSO algorithm with gravitational search algorithm in order to solve the ANNs training problem. The new proposed training method reduced the problem of falling in local minima and slow convergence. The adopted method was compared to the other approaches. The results showed that the proposed method outperformed the others [7]. [8] considered taking advantage of both local search and global search algorithms. They merged PSO with the back-propagation algorithm. The statistical results proved PSO as a robust algorithm for training ANNs. The authors compared another method using modified differential evolution algorithm in order to train ANNs. In [9], the researchers used an adaptive differential evolution algorithm to train ANNs. The aim of the calculation is to find optimal weights. Adopted algorithm compared with the other method for different classification problems [9]. (Das et al., 2014) [10] used PSO algorithm for training ANNs for solving the problem of equalization channel. The result showed that the proposed equalizer achieved better results than the fuzzy equalizers in all conditions [10].[11] used both PSO algorithm and another important nature inspired algorithm, ANN-Bee colony

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Training Neural Networks with Krill Herd Algorithm

Cited by 49 publications

References 20 publications

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

On the Use of Nature Inspired Metaheuristic in Computer Game

The Effect of Bat Algorithm and Genetic Algortihm on the Training Performance of Artificial Neural Networks

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