Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

2016 ◽  
pp. 362-374 ◽  
Author(s):  
Ashraf M. Abdelbar ◽  
Islam Elnabarawy ◽  
Donald C. Wunsch II ◽  
Khalid M. Salama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
High Order ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Test Set ◽  
Benchmark Datasets ◽  
Gradient Based ◽  
High Order Neural Network

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOR, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOR to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOR has better test set generalization than R-Prop, though not to a statistically significant extent.

Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

2020 ◽  
pp. 82-95
Author(s):  
Ashraf M. Abdelbar ◽  
Islam Elnabarawy ◽  
Donald C. Wunsch II ◽  
Khalid M. Salama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
High Order ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Test Set ◽  
Benchmark Datasets ◽  
Gradient Based ◽  
High Order Neural Network

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOℝ, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOℝ to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOℝ has better test set generalization than R-Prop, though not to a statistically significant extent.

scholarly journals A Novel Wireless Sensor Node Positioning Algorithm Based on Ant Colony Optimization Algorithm and Neural Network

2018 ◽  
Vol 14 (10) ◽  
pp. 40
Author(s):  
Beichen Chen
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Cluster Structure ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Ant Colony Optimization Algorithm ◽  
Positioning Accuracy ◽  
Bee Colony ◽  
Positioning Algorithm

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to enhance the positioning accuracy of wireless sensor network (WSN) nodes. For this purpose, a WSN node positioning algorithm was proposed based on artificial bee colony (ABC) algorithm and the neural network (NN). First, the parameters between three anchor nodes and the target node were measured. Then, the ABC and NN were introduced to simulate and predict the ranging error, and the weight was determined according to the results. In the proposed algorithm, the cluster structure was effectively combined with the NN model. The weight of backpropagation NN was optimized by the ant colony optimization (ACO) algorithm. Then, the ACO-optimized NN was used to fuse the data collected by WSN nodes. The simulation results show that the proposed algorithm can improve the positioning accuracy of WSN nodes and reduce the time of the search. The research findings shed new light on the positioning of WSN nodes.</span>

New Evolutionary Neural Network Based on Continuous Ant Colony Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1773-1778
Author(s):  
Wei Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Evolutionary Programming ◽  
Ant Colony ◽  
Evolutionary Neural Network ◽  
Evolutionary Neural Networks ◽  
Xor Problem

The evolutionary neural network can be generated combining the evolutionary optimization algorithm and neural network. Based on analysis of shortcomings of previously proposed evolutionary neural networks, combining the continuous ant colony optimization proposed by author and BP neural network, a new evolutionary neural network whose architecture and connection weights evolve simultaneously is proposed. At last, through the typical XOR problem, the new evolutionary neural network is compared and analyzed with BP neural network and traditional evolutionary neural networks based on genetic algorithm and evolutionary programming. The computing results show that the precision and efficiency of the new neural network are all better.

Data Mining using Advanced Ant Colony Optimization Algorithm and Application to Bankruptcy Prediction

2014 ◽  
pp. 1554-1576
Author(s):  
Vishal Arora ◽  
Vadlamani Ravi
Keyword(s):  
Data Mining ◽  
Ant Colony Optimization ◽  
Predictive Accuracy ◽  
Bankruptcy Prediction ◽  
Ant Colony ◽  
Data Repository ◽  
Ant Colony Optimization Algorithm ◽  
Data Mining Technique ◽  
Benchmark Datasets ◽  
Area Under Roc Curve

Ant Colony Optimization (ACO) is gaining popularity as data mining technique in the domain of Swarm Intelligence for its simple, accurate and comprehensive nature of classification. In this paper the authors propose a novel advanced version of the original ant colony based miner (Ant-Miner) in order to extract classification rules from data. They call this Advanced ACO-Miner (ADACOM). The main goal of ADACOM is to explore the flexibility of using a different knowledge extraction heuristic approach viz. Gini's Index to increase the predictive accuracy and the simplicity of the rules extracted. Further, the authors increase the information and the prediction level of the set of rules extracted by dynamically changing specific parameters. Simulations are performed with ADACOM on a few benchmark datasets Wine, WBC (Wisconsin Breast Cancer) and Iris from UCI (University of California at Irvine) data repository and compared with Ant-Miner (Parpinelli, Lopes, & Freitas, 2002), Ant-Miner2 (Liu, Abbass, & McKay, 2002), Ant-Miner3 (Liu, Abbass, & McKay, 2003), Ant-Miner+ (Martens, De Backer, Haesen, Vanthienen, Snoeck, & Baesens, 2007) and C4.5 (Quinlan, 1993). The results show that ADACOM outperforms the above mentioned algorithms in terms of predictive accuracy, simplicity of rules, sensitivity, specificity and AUC values (area under ROC curve). In addition, the ADACOM is also employed to extract rules from bank datasets (UK, US, Spanish and Turkish) for bankruptcy prediction and the results are compared with that obtained by Ant-Miner. Again ADACOM yielded better results and is proven to be the better choice for solving bankruptcy prediction problems in banks

scholarly journals Ant colony optimization based founder sequence reconstruction

2018 ◽  
Vol 33 (2) ◽  
Author(s):  
Anh Vu Thi Ngoc ◽  
Dinh Phuc Thai ◽  
Hoang Duc Nguyen ◽  
Thanh Hai Dang ◽  
Dong Do Duc
Keyword(s):  
Ant Colony Optimization ◽  
Evolutionary Biology ◽  
Optimization Problem ◽  
Gene Sequence ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Benchmark Datasets ◽  
Sequence Reconstruction ◽  
Test Sets ◽  
Founder Gene

Reconstruction of founder (ancestor) genes for a given population is an important problem in evolutionary biology. It involves finding a set of genes that can combine together to form genes of all individuals in that population. Such reconstruction can be modeled as a combinatorial optimization problem, in which we have to find a set of founder (gene) sequences so that the individuals in a given population can be generated by the smallest number of recombination on these founder sequences. In this paper we propose a novel ant colony optimization algorithm (ACO) based method, equipped with some important improvements, for the founder gene sequence reconstruction problem. The proposed method yields excellent performance when validating on 108 test sets from three benchmark datasets. Comparing with the best by far method for founder sequence reconstruction, our proposed method performs better in 45 test sets, equally well in 44 and worse only in 19 sets. These experimental results demonstrate the efficacy and perspective of our proposed method.

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