The Applications Survey on Bee Colony Optimization

Studies in Computational Intelligence, 2009

Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The Bee Colony Optimization (BCO) metaheuristic has been introduced fairly recently as a new direction in the field of Swarm Intelligence. Artificial bees represent agents, which collaboratively solve complex combinatorial optimization problem. The chapter presents a classification and analysis of the results achieved using Bee Colony Optimization (BCO) to model complex engineering and management processes. The primary goal of this chapter is to acquaint readers with the basic principles of Bee Colony Optimization, as well as to indicate potential BCO applications in engineering and management. 1 Introduction Many species in the nature are characterized by swarm behavior. Fish schools, flocks of birds, and herds of land animals are formed as a result of biological needs to stay together. Individuals in herd, fish school, or flock of birds has a higher probability to stay alive, since predator usually assault only one individual. A collective movement characterizes flocks of birds, herds of animals, and fish schools. Herds of animals respond quickly to changes in the direction and speed of their neighbors. Swarm behavior is also one of the main characteristics of social insects (bees, wasps, ants, termites). Communication between individual insects in a colony of social insects has been well known. The communication systems between individual insects contribute to the configuration of the ''collective intelligence" of the social insect colonies. The term ''Swarm intelligence", that denotes this ''collective intelligence" has come into use [1], [2], [3], [4]. Swarm Intelligence [4] is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. These decentralized systems (Multi Agent Systems) are composed of physical individuals (robots, for example) or "virtual" (artificial) ones that communicate among themselves, cooperate, collaborate, exchange information and knowledge and perform some tasks in their environment. Marković, Teodorović, and Aćimović-Raspopović BCO Routing and wavelength assignment in all-optical networks Wedde,

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the application of ABC, with the expectation that it would serve as a reference material to both old and new, incoming researchers to the field, to support their understanding of current trends and assist their future research prospects and directions. The advantages, applications and drawbacks of the newly developed ABC hybrids are highlighted, critically analyzed and discussed accordingly.

The Bee Colony Optimization (BCO) meta-heuristic belongs to the class of Nature-Inspired Algorithms. This technique uses an analogy between the way in which bees in nature search for a food, and the way in which optimization algorithms search for an optimum in combinatorial optimization problems. Artificial bees represent agents, which collaboratively solve complex combinatorial optimization problem. The chapter presents a description of the algorithm, classification and analysis of the results achieved using Bee Colony Optimization (BCO) to model complex engineering and management processes.

Artificial Bee Colony Algorithm (ABC) is nature-inspired metaheuristic, which imitates the foraging behavior of bees. ABC as a stochastic technique is easy to implement, has fewer control parameters, and could easily be modify and hybridized with other metaheuristic algorithms. Due to its successful implementation, several researchers in the optimization and artificial intelligence domains have adopted it to be the main focus of their research work. Since 2005, several related works have appeared to enhance the performance of the standard ABC in the literature, to meet up with challenges of recent research problems being encountered. Interestingly, ABC has been tailored successfully, to solve a wide variety of discrete and continuous optimization problems. Some other works have modified and hybridized ABC to other algorithms, to further enhance the structure of its framework. In this review paper, we provide a thorough and extensive overview of most research work focusing on the app...

2011

… Network Applications in …, 2006

Various natural systems lecture us that very simple individual organisms can create systems able to perform highly complex tasks by dynamically interacting with each other. The Bee Colony Optimization Metaheuristic (BCO) is proposed in this paper. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. The agents use approximate reasoning and rules of fuzzy logic in their communication and acting. The BCO is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty.

International Journal of Engineering & Technology, 2013

In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

International Journal of Advanced Intelligence Paradigms, 2013

In recent years, swarm intelligence has proven its importance for the solution of those problems that cannot be easily dealt with classical mathematical techniques. The foraging behaviour of honey bees produces an intelligent social behaviour and falls in the category of swarm intelligence. Artificial bee colony (ABC) algorithm is a simulation of honey bee foraging behaviour, established by Karaboga in 2005. Since its inception, a lot of research has been carried out to make ABC more efficient and to apply it on different types of problems. This paper presents a review on ABC developments, applications, comparative performance and future research perspectives.

2018

Swarm intelligence has provided solution to classical mathematical techniques which can’t be dealt easily. An intelligent foraging behavior of honey bees falls into category of swarm intelligence. Deception of honey bee foraging behavior of Artificial Bee Colony(ABC) was established in 2005. Due to its origination, researches had been made on ABC and applied it on different types of problems. In this article, a brief review is presenting about ABC and its progress, implementation, comparative completion and future scrutinizing perspectives. We focused and presented on recent works by ABC and It’s applications.

Yugoslav Journal of Operations Research, 2014

This paper is an extensive survey of the Bee Colony Optimization (BCO) algorithm, proposed for the first time in 2001. BCO and its numerous variants belong to a class of nature-inspired meta-heuristic methods, based on the foraging habits of honeybees. Our main goal is to promote it among the wide operations research community. BCO is a simple, but efficient meta-heuristic technique that has been successfully applied to many optimization problems, mostly in transport, location and scheduling fields. Firstly, we shall give a brief overview of the other meta-heuristics inspired by bees? foraging principles pointing out the differences between them. Then, we shall provide the detailed description of the BCO algorithm and its modifications, including the strategies for BCO parallelization, and giving the preliminary results regarding its convergence. The application survey is elaborated in Part II of our paper.

Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI), 2021

Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. Optimization contributes to optimal resource management by way of efficient and effective problem-solving. Engineers' attention has been driven to more effective and scalable metaheuristic algorithms as a result of the complicated optimization issues. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. The authors see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores several algorithms such as ACO, PSO, GA, and FA.

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

Scholarpedia, 2010

The Artificial Bee Colony is a swarm based meta-heuristic algorithm for optimizing numerical based problem and provides the solution with accuracy and less effort, cost, time and space. Here the word meta heuristic means to provide a better solution to the given problem especially in the case of incomplete or imperfect information. In ABC algorithm, bees search best food source from many food sources and during searching best source, it consider various parameter like nectar amount (fitness value of food source), time etc. This algorithm was inspired by the foraging behavior of honey bees swarm, to look for the best solution to an optimization problem. The model which uses this algorithm has 3 essential components: Employed bees, Unemployed bees and the food sources. The employed or scout bees basically wander for the rich food sources which can be close to their hive. In this algorithm, a crowd of artificial forager bees that are the agents of the environment starts searches for the rich food sources i.e. the good solution for the given problem. and to apply this algorithm, the examined optimization problem is first transformed to the problem of analyzing the best parameter vectors for the given array vectors to minimizes the objective function. Then the artificial bees randomly and unexpectedly find a population of initial solution vectors which can be then improved by employing some strategies i.e. forwarding towards the better solutions in terms of neighbor search parameter mechanism.