Bee Colony Optimization

Web spam is the deliberate manipulation of search engine indexes.web spam involves a number of methods, such as repeating unrelated phrases, to manipulate the relevance or prominence of resources indexed in a manner inconsistent with the purpose of the indexing system. Search engine includes determining whether the search term appears in the content or URL of a webpage. We presents a spam host detection approach. The content and link features are extracted from hosts to train a learning model based on ant colony optimization (ACO) and bee colony optimization (BCO) algorithm. The dataset has been collected from WEBSPAM-UK2007 and implemented by java Environment. The optimal solution is compared with the ant colony and bee colony optimization. Finally, it provides which optimization algorithm is better in detecting spam. Keywords:––Ant colony optimization, Bee colony optimization, content and link features and spam host detection

The European honey bee, Apis mellifera, is well-known for its role in honey production, its ecological importance in plant reproduction and biodiversity, and more importantly, the pollination of many economically significant crops in the United States (Staveley, Law, Fairbrother, & Menzie, 2014; vanEngelsdorp & Meixner, 2010). The food we eat from the most nutritious crops in our diets – key fruits and vegetables – as well as some crops used as fodder in meat and dairy production significantly depend on the key ecosystem service that honey bees  provide: natural insect-mediated pollination (Tirado, Simon, & Johnston, 2013). As the predominant and most economically important group of pollinators in most geographical regions, both managed and wild honey bees pollinate most wild plants so that they may reproduce, thereby directly or indirectly supporting wild habitats that provide other ecosystem services (Tirado et al., 2013). One can then see the possible ramifications of the ongoing bee colony decline in recent years; however, the economic and ecological consequences tend to be viewed separately in their own scope, and it is not entirely clear how the two areas intersect.

—In this paper an overview of the areas where the Bee Colony Optimization (BCO) and its variants are applied have been given. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO) was identified by Lucic and Teodorovic in 2001. BCO has emerged as a specialized class of Swarm Intelligence with bees as agents. It is an emerging field for researchers in the field of optimization problems because it provides immense problem solving scope for combinatorial and NP-hard problems. BCO is one of the benchmark systems portraying team work, collaborative work. BCO is a bottom-up approach of modeling where agents form global solution by optimizing the local solution.

Optimization techniques are stimulated by Swarm Intelligence wherever the target is to get a decent competency of a problem. The knowledge of the behavior of animals or insects has a variety of models in Swarm Intelligence. Swarm Intelligence has become a potential technique for evolving many robust optimization problems. Researchers have developed various algorithms by modeling the behaviors of the different swarm of animals or insects. This paper explores three existing meta-heuristic methods named as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) and Bat Algorithm (BA). Ant Colony Optimization was stimulated by the nature of ants. Bee Colony Optimization was inspired by the plundering behavior of honey bees. Bat Algorithm was emerged on the echolocation characteristics of micro bats. This study analyzes the problem-solving behavior of groups of relatively simple agents wherein local interactions among agents, are either directly or indirectly through the environment. The scope of this paper is to explore the characteristics of swarm intelligence as well as its advantages, limitations and application areas, and subsequently, to explore the behavior of ants, bees and micro bats along with its most popular variants. Furthermore, the behavioral comparison of these three techniques has been analyzed and tried to point out which technique is better for optimization among them in Swarm Intelligence. From this, the paper can help to understand the most appropriate technique for optimization according to their behavior.

Swarm intelligence models are referred to as computational models inspired by natural swarm systems. To date, several swarm intelligence models based on different natural swarm systems have been proposed in the literature, and successfully applied in many real-life applications.

According to the increase of using data mining techniques in improving educational systems operations, Educational Data Mining has been introduced as a new and fast growing research area. Educational Data Mining aims to analyze data in educational environments in order to solve educational research problems. In this paper a new associative classification technique has been proposed to predict students final performance. Despite of several machine learning approaches such as ANNs, SVMs, etc. associative classifiers maintain interpretability along with high accuracy. In this research work, we have employed Honeybee Colony Optimization and Particle Swarm Optimization to extract association rule for student performance prediction as a multi-objective classification problem. Results indicate that the proposed swarm based algorithm outperforms well-known classification techniques on student performance prediction classification problem.

One of the successful methods in classification problems is feature selection. Feature selection algorithms; try to classify an instance with lower dimension, instead of huge number of required features, with higher and acceptable accuracy. In fact an instance may contain useless features which might result to misclassification. An appropriate feature selection methods tries to increase the effect of significant features while ignores insignificant subset of features. In this work feature selection is formulated as an optimization problem and a novel feature selection procedure in order to achieve to a better classification results is proposed. Experiments over a standard benchmark demonstrate that applying Bee Colony Optimization in the context of feature selection is a feasible approach and improves the classification results.

The use of various topical compounds for blood biting bug protection has been documented throughout written history with DEET being the current standard in chemical protection. While many minimize the danger of using DEET, it is no longer considered as completely safe to use, especially for those who are pregnant, nursing or infants. This study documents a significant change in addressing protection from haematophagous arthropods (blood seeking insects) with the use of a formulated oral solution designed to activate specific targets in the body's immunity system. By association, this reveals new and natural solutions for disrupting the pathways of disease carrying vectors. This study demonstrates and documents the efficacy of a novel biomineral complex, with a designed catalyst, that activates the human/animal created putative cloaking compounds, letting the inherent ability of the immune system create different concentrations and ratios of volatile compounds [4], to disrupt the blood seeking behavior of mosquitoes and other haematophagous arthropods. By stimulating the immune system, the body is able to protect itself instead of requiring the application of potentially harmful topical repellents. The delivery of haematophagous arthropod protection has always been problematic with the current chemical topical repellents being either toxic or very limited in their effectiveness [11] [13] [14] [15]. The NNSciences team looked at the challenge of delivering protection along a different pathway. While existing research proves that volatile vapours release semio-chemicals that create highly effective repellency [4] [5], it was limited to topical applications. The NNS team incorporated a different strategy, instead focusing on the inside of the human body, where there is already a set of NATURE NATURALLY SCIENCES All rights reserved

Artykuł zawiera prezentację wyników bardzo specjalnego eksperymentu symulacyjnego. Otóż korzystając z wcześniej opracowanego i przetestowanego w konfrontacji z rzeczywistym obiektem biologicznym biocybernetycznego modelu rodziny pszczelej przeprowadzono symulację rozwoju i funkcjonowania w okresie całego roku tej samej rodziny pszczelej (z tą samą matką pszczelą, w tych samych symulowanych zjawiskach meteorologicznych i fenologicznych (zmiany zasobności środowiska w rośliny miododajne) ale przy różnych przy różnych warunkach początkowych (wielkości zapasów posiadanych przez rodzinę pszczelą w dniu pierwszego wiosennego oblotu). Wykazano, że niekiedy, przy szczególnie małych zapasach, rodzina pszczela może nie przeżyć i zginie w trakcie sezonu, ale w przeciwnym przypadku różnica warunków początkowych praktycznie nie wpływa na końcowe rezultaty (liczbę żywych pszczół, wielkości zapasów, powierzchnie zbudowanych plastrów itp.)

W artykule opisano cybernetyczny (matematyczny) model rodziny pszczelej i wskazano na możliwości jego wykorzystania w praktyce. Model symulacyjny zbudowany w oparciu o ten model cybernetyczny pozwala przewidywać zjawiska dynamiczne w złożonym systemie, jakim jest rodzina pszczela, oraz sterować nimi w celu osiągania zamierzonych efektów - na przykład maksymalnej liczby pszczół zapylających sady w momencie ich kwitnienia.

Tasks scheduling problem is a key factor for distributed systems to gain better performance. Even in the best conditions, the scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperform the existent GA-based method in term of Makespan.

The development of a genus depends always on the environment in which it lives and all the conditions which exist in its domain of activity. Almost always in the nature there are some rival dependencies as the results of competition for food. The paper presents an attempt at modelling description of the passive competition phenomenon among the bee families concerning a common source of food. This phenomenon has been analyzed to show the rules of division of limited food reserve and the use of this reserve. The influences of following factors have been discussed: —strength of a bee family, efficiency, location and the "attraction" of a food source,— searching, transmission and the exchange of information, —influences of random factors, weather etc. A digital simulation model realized on the Odra 1300 and R-32 computers has been used.

MANET's use wireless medium for communication, thus two nodes arranged in a multi-hop fashion can communicate directly if and only if they are within their transmission radius. MANET's have adopted many conventional routing algorithms, swarm intelligence (SI) has recently captured much attention because many new researches have proposed its adoption for MANET routing. Many researchers have proposed numerous Swarm Intelligence (SI) based algorithm. In this paper routing protocols for MANET are discussed which are bio-exhilarated. This paper also discusses various optimization techniques which have been used for routing in MANET's.

In this paper an overview of the areas where the Bee Colony Optimization (BCO) and its variants are applied have been given. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO) was identified by Lucic and Teodorovic in 2001. BCO has emerged as a specialized class of Swarm Intelligence with bees as agents. It is an emerging field for researchers in the field of optimization problems because it provides immense problem solving scope for combinatorial and NP-hard problems. BCO is one of the benchmark systems portraying team work, collaborative work. BCO is a bottom-up approach of modeling where agents form global solution by optimizing the local solution.

Constructive Cost Model (COCOMO) used parameters for software effort estimation, which were calculated in 1981 by
regression analysis of 63 types of project data; therefore applying these parameters to current project development
will not generate accurate results. The objective of current research is applying Bee Colony Optimization (BCO) metaheuristic
approach to optimize the parameters of COCOMO model for improving software cost estimation. The Bee Colony
Optimization (BCO) is a new branch of Swarm Intelligence and has been applied successfully to various engineering
disciplines. BCO approach is a “bottom-up” approach to modeling where special kinds of artificial agents are created by
analogy with bees. These artificial agents or bees are used to solve complex combinatorial optimization problems. The
proposed model validation is carried out using Interactive Voice Response software project dataset of a company. The
results generated by the proposed model are compared to those obtained by methods proposed in the literature using
Walston-Felix, SEL, Bailey-Basil, COCOMO II and Halstead models. The BCO approach generates various partial solutions
and best solution is selected based on Mean Magnitude of Relative Error. The results obtained show that the proposed BCO
based model is able to improve the accuracy of cost estimation and also outperform other models.