Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.Comment: 14 Pages, 11 figure

Quick Combinatorial Artificial Bee Colony -qCABC- Optimization Algorithm for TSP

Combinatorial Artificial Bee Colony Algorithm (CABC) is a new version of Artificial Bee Colony (ABC) to solve combinatorial type optimization problems and quick Artificial Bee Colony (qABC) algorithm is an improved version of ABC in which the onlooker bees behavior is modeled in more detailed way. Studies showed that qABC algorithm improves the convergence performance of standard ABC on numerical optimization. In this paper, to see the performance of this new modeling way of onlookers' behavior on combinatorial optimization, we apply the qABC idea to CABC and name this new algorithm as quick CABC (qCABC). qCABC is tested on Traveling Salesman Problem and simulation results show that qCABC algorithm improves the convergence and final performance of CABC

A Survey on Load Balancing in Cloud Computing Using Optimization Technique

The main goal of this work is to create a system that uses the improved Ant Colony and Artificial Bee Colony (AB) algorithm to provide load balancing for the cloud computing technology. This algorithm is a combination of Ant colony algorithm and Artificial Bee colony algorithm. It will improve the existing AB algorithm. There are certain limitations in the existing algorithm. This algorithm will overcome those limitations and provide good optimal solution for effective load balancing

An Efficient Universal Bee Colony Optimization Algorithm

The artificial bee colony algorithm is a global optimization algorithm. The artificial bee colony optimization algorithm is easy to fall into local optimal. We proposed an efficient universal bee colony optimization algorithm (EUBCOA). The algorithm adds the search factor u and the selection strategy of the onlooker bees based on local optimal solution. In order to realize the controllability of algorithm search ability, the search factor u is introduced to improve the global search range and local search range. In the early stage of the iteration, the search scope is expanded and the convergence rate is increased. In the latter part of the iteration, the algorithm uses the selection strategy to improve the algorithm accuracy and convergence rate. We select ten benchmark functions to testify the performance of the algorithm. Experimental results show that the EUBCOA algorithm effectively improves the convergence speed and convergence accuracy of the ABC algorithm

Global gbest guided-artificial bee colony algorithm for numerical function optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

A hybrid of bacterial foraging and differential evolution -based distance of sequences

AbstractIn a previous work we presented a new distance that we called the sigma gram distance, which is used to compute the similarity between two sequences. This distance is based on parameters which we computed through an optimization process that used the artificial bee colony; a bio-inspired optimization algorithm. In this paper we show how a hybrid of two optimization algorithms; bacterial foraging and differential evolution, when used to compute the parameters of the sigma gram distance, can yield better results than those obtained by applying artificial bee colony. This superiority in performance is validated through experiments on the same data sets to which artificial bee colony, on the same optimization problem, was tested

A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc

A Binomial Crossover Based Artificial Bee Colony Algorithm for Cryptanalysis of Polyalphabetic Cipher

Cryptography is one of the common approaches to secure private data and cryptanalysis involves breaking down a coded cipher text without having the key. Cryptanalysis by brute force cannot be accepted as an effective approach and hence, metaheuristic algorithms performing systematic search can be applied to derive the optimal key. In this study, our aim is to examine the overall suitability of Artificial Bee Colony algorithm in the cryptanalysis of polyalphabetic cipher. For this purpose, using a number of different key lengths in both English and Turkish languages, basic Artificial Bee Colony algorithm (ABC) is applied in the cryptanalysis of Vigenere cipher. In order to improve the ABC algorithm\u27s convergence speed, a modified binomial crossover based Artificial Bee Colony algorithm (BCABC) is proposed by introducing a binomial crossoverbased phase after employed bee phase for a precise search of global optimal solution. Different keys in various sizes, various cipher texts in both English and Turkish languages are used in the experiments. It is shown that optimal cryptanalysis keys produced by BCABC are notably competitive and better than those produced by basic ABC for Vigenere cipher analysis

Analisis dan Implementasu Algoritma Artificial Bee Colony pada Optimasi Model Bisnis PT. Unilever Indonesia divisi Wall Ice Cream

ABSTRAKSI: Artificial Bee Colony merupakan algoritma optimasi untuk menemukan nilai lebih kecil atau lebih besar yang berbasis pada swarm intelligences secara probabilistik. Dalam penelitian ini dilakukan pengujian dengan menggunakan metode algoritma Artificial Bee Colony untuk mencari nilai-nilai optimal dalam suatu data model bisnis, sebagai acuan dalam analisis perusahaan untuk menentukan model bisnis yang digunakan. Data masukan berupa matriks keterkaitan antar variable model bisnis yang memiliki bobot, yang kemudian diolah kedalam rumus objektif yang dimiliki oleh data numerik untuk mencari nilai optimal yang mungkin pada suatu rentang nilai tertentu.Pengujian didasarkan atas keluaran algoritma optimasi dalam menentukan solusi untuk suatu data numeric.Berdasarkan hasil penelitian, Algorima Optimasi Artificial Bee colony dapat diterapkan untuk mencari solusi nilai optimal bagi model bisnis.Kata Kunci : Kata kunci: Artificial Bee colony, Swarm Inteligence, Model Bisnis, Algoritma Optimasi, Matriks KeterkaitanABSTRACT: Artificial Bee Colony is the one of optimization algorithm with benefit to find optimal value with swarm intelligences with probabilistic method. In this research writers use Artificial Bee Colony Algorithm to find the optimal business model for a company, with benefit to direct the company to get the best model business. Data based on adjacency matrics between business models variables which has value. The data processed into function to gets new solution.Training is used to find the output of data metric optimally. Based on research, Artificial Bee Algorithm can be implemented to find the optimal solution for business model.Keyword: Keywords: Artificial Bee colony, Swarm Inteligence, Business Model, Algoritma Optimasi, Adjacency matri