BQIABC: A new Quantum-Inspired Artificial Bee Colony Algorithm for Binary Optimization Problems

Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper a new binary version of the ABC algorithm inspired by quantum computing, called binary quantum-inspired artificial bee colony algorithm (BQIABC), is proposed. The BQIABC combines the main structure of ABC with the concepts and principles of quantum computing such as, quantum bit, quantum superposition state and rotation Q-gates strategy to make an algorithm with more exploration ability. The proposed algorithm due to its higher exploration ability can provide a robust tool to solve binary optimization problems. To evaluate the effectiveness of the proposed algorithm, several experiments are conducted on the 0/1 knapsack problem, Max-Ones and Royal-Road functions. The results produced by BQIABC are compared with those of ten state-of-the-art binary optimization algorithms. Comparisons show that BQIABC presents the better results than or similar to other algorithms. The proposed algorithm can be regarded as a promising algorithm to solve binary optimization problems

Comparison of Intelligent Systems, Artificial Neural Networks and Neural Fuzzy Model for Prediction of Gas Hydrate Formation Rate

The main objective of this study was to present a novel approach for predication of gas hydrate formation rate based on the Intelligent Systems. Using a data set including about 470 data obtained from flow tests in a mini-loop apparatus, different predictive models were developed. From the results predicted by these models, it can be pointed out that the developed models can be used as powerful tools for prediction of gas hydrate formation rate with total errors of less than 4%

The fifth developing plan of Iranian Fisheries Research Institute

Accurate scientific and practical plan for achieving the goals of the Islamic Republic of Iran within the framework of Vision development 1404, is the infrastructure achieving sustainable development of the country. Order to achieve the above mentioned objectives and in order to the comprehensive development plans in the country, Iranian fisheries research organization adjust the fifth developing plan for support of executive related departments in country with mobilization a large number researchers consists of several working groups of ifro affiliated research centers. The fifth developing plan consist of three chapters for report of the forth developing plan and intrudction of research, construction plans and financial support (budjet) for period of 2011-2014 A.C

Improving n-Similarity Problem by Genetic Algorithm and Its Application in Text Document Resemblance

In this paper, some methods of similarity measures between objects are presented with their properties reviewed. The study is conducted to propose a new method based on genetic algorithm in order to reduce the time complexity of finding n most similar objects among the huge number of objects. This method is tested on two applications. The former aims at finding the most similar residents in a condominium, and the latter deals with finding the most similar n-groups of text documents out of a great dataset. The simulation results show that the proposed method can efficiently improve the order of time complexity especially for the second application

Artificial ants to extract leaf outlines and primary venation patterns

This paper presents preliminary results on an investigation into using artificial swarms to extract and quantify features in digital images. An ant algorithm has been developed to automatically extract the outlines and primary venation patterns from digital images of living leaf specimens via an edge detection method. A qualitative and quantitative analysis of the results is carried out herein. The artificial swarms are shown to converge onto the edges within the leaf images and statistical accuracy, as measured against ground truth images, is shown to increase in accordance with the swarm convergence. Visual results are promising, however limitations due to background noise need to be addressed for the given application. The findings in this study present potential for increased robustness in using swarm based methods, by exploiting their stigmergic behaviour to reduce the need for parameter fine-tuning with respect to individual image characteristics. © 2008 Springer-Verlag Berlin Heidelberg