Swarm Intelligence Based Protein Conformational Search Algorithm.

There is no doubt of the role that proteins play in the biological processes inside the human body. Proteins can perform their function only when they fold into their tertiary structure. The thermodynamics hypothesis formulated by Anfinsen stated that the tertiary structure of a protein in its physiological environment is the conformation with the lowest free energy

HABCO: A Robust Agent on Hybrid Ant-Bee Colony Optimization

The purpose of this research is to generate a robust agent by combining bee colony optimization (BCO) and ELU-Ants for solving traveling salesman problem (TSP), called HABCO. The robust agents, called ant-bees, firstly are grouped into three types scout, follower, recruiter at each stages. Then, the bad agents are high probably discarded, while the good agents are high probably duplicated in earlier steps. This first two steps mimic BCO algorithm. However, constructing tours such as choosing nodes, and updating pheromone are built by ELU-Ants method.To evaluate the performance of the proposed algorithm, HABCO is performed on several benchmark datasets and compared to ACS and BCO. The experimental results show that HABCO achieves the better solution, either with or without 2opt

Simulation and Management System for Beekeepers

Tato bakalářská práce se zabývá analýzou, návrhem a implementací systému pro evidenci a simulaci stavu včelstev. Evidenční systém umožňuje ukládání, aktualizaci a prohlížení stavu včelstev a je navržen především pro nástavkové typy úlů. Simulační model je zaměřen na simulaci medové snůšky a s evidenčním systémem je do jisté míry provázán. V závislosti na nastavených parametrech simulačního modelu jsou při simulaci zobrazována místa, kam by včely pravděpodobně létaly za snůškou, a celkový výnos medu za jeden den.This bachelor thesis deals with analysis, design and implementation of the management and simulation system for beekeepers. The management system provides storing, updating and browsing the state of bee hives and is designed for the type of hives with removable frames. The simulation system contains the model of collecting nectar by honey bees and is addapted to management system. Depending on parameters of simulation model the application visualizes locations where would honey bees probably fly for the nectar and shows the amount of collected honey in one day.

A New Method for Solving Supervised Data Classification Problems

Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm

An efficient robust hyperheuristic clustering algorithm

Observations on recent research of clustering problems illustrate that most of the approaches used to deal with these problems are based on meta-heuristic and hybrid meta-heuristic to improve the solutions. Hyperheuristic is a set of heuristics, meta- heuristics and high-level search strategies that work on the heuristic search space instead of solution search space. Hyperheuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. In the last few years, most studies have focused considerably on the hyperheuristic algorithms to find generalized solutions but highly required robust and efficient solutions. The main idea in this research is to develop techniques that are able to provide an appropriate level of efficiency and high performance to find a class of basic level heuristic over different type of combinatorial optimization problems. Clustering is an unsupervised method in the data mining and pattern recognition. Nevertheless, most of the clustering algorithms are unstable and very sensitive to their input parameters. This study, proposes an efficient and robust hyperheuristic clustering algorithm to find approximate solutions and attempts to generalize the algorithm for different cluster problem domains. Our proposed clustering algorithm has managed to minimize the dissimilarity of all points of a cluster using hyperheuristic method, from the gravity center of the cluster with respect to capacity constraints in each cluster. The algorithm of hyperheuristic has emerged from pool of heuristic techniques. Mapping between solution spaces is one of the powerful and prevalent techniques in optimization domains. Most of the existing algorithms work directly with solution spaces where in some cases is very difficult and is sometime impossible due to the dynamic behavior of data and algorithm. By mapping the heuristic space into solution spaces, it would be possible to make easy decision to solve clustering problems. The proposed hyperheuristic clustering algorithm performs four major components including selection, decision, admission and hybrid metaheuristic algorithm. The intensive experiments have proven that the proposed algorithm has successfully produced robust and efficient clustering results

Identification of potential biomarker genes for selecting varroa tolerant honey bees (Apis mellifera) and biochemical characterization of a differentially expressed carboxylesterase gene in response to mite infestation

Previously a large number of differentially expressed genes were identified by a DNA microarray analysis of two contrasting honey bee colonies for tolerance and susceptibility to varroa mite infestation. This study initially analyzed the expression patterns of ten of these genes in detail for a wide range of colonies with a range of phenotypes for susceptibility and tolerance to varroa mite infestation using real time qRT-PCR. Dark eyed stage 4 pupae with and without varroa infestation were sampled for the molecular analysis. The results showed that three out of the ten genes, AmCbE E4, AmApoD and AmCYP6A1 displayed relatively consistent differential expression patterns among the colonies and could be used as potential biomarkers for identifying varroa tolerant colony phenotypes. In general, these biomarker genes exhibited higher expression in tolerant colonies and lower expression in susceptible colonies with varroa mite infestation, compared to non-infested colonies. Tissue expression analysis showed AmCbE E4 was more differentially expressed in the head and AmApoD was differentially expressed in the abdomen, and AmCYP6A1 showed more differential expression in the thorax and abdomen among the honey bees differing in varroa tolerance and susceptibility. Expression of the three genes also responded to miticide treatments in the colonies. The miticide treatments (Apistan®, Apivar®, Thymovar®) could stimulate their expression in tolerant colonies, but not in susceptible colonies. In addition, the infection of deformed wing virus (DWV), another biotic stressor for honey bees primarily vectored by the mite, was also quantitatively evaluated by real time qRT-PCR in the varroa tolerant and susceptible honey bee colonies. The results showed that DWV infections were considerably increased in the susceptible colonies infested by varroa mites or treated with miticides (Apistan®, Apivar®, Thymovar®). AmCbE E4 encoding a putative esterase E4 was identified for its highly differential expression between the susceptible and tolerant bees in response to the mite infestation. Its biochemical function was analyzed by cloning the AmCbE E4 from the head of the dark eyed stage 4 pupae and heterologously expressing it in E. coli. The enzymatic assays revealed that AmCbE E4 could hydrolyze synthetic esterase substrates, α-naphthyl acetate, β-naphthyl acetate and para-nitrophenyl acetate, as well as carbaryl, a carbamate pesticide. This result suggests a defensive function of AmCbE E4 in protecting the varroa tolerant bees from the toxic stresses of carboxylester miticides and ester compounds possibly produced by the Varroa destructor parasitism

Clustering analysis using Swarm Intelligence

This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), to automatic grouping of large unlabeled datasets. ∙ Define partitional clustering as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. For that purpose, this thesis is divided is five main parts: ∙ The first part, including Chapter 1, aims at introducing state of the art of swarm intelligence based clustering methods. ∙ The second part, including Chapter 2, consists in clustering analysis with combination of artificial bee colony algorithm and K-means technique. ∙ The third part, including Chapter 3, consists in a presentation of clustering analysis using opposition-based API algorithm. ∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis using particle swarm optimization. ∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses the future directions and the open issues of this research