Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

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.

Artificial bee colony algorithm, its variants and applications: A survey.

Over past few decades, families of algorithms based on the intelligent group behaviors of social creatures like ants, birds, fishes, and bacteria have been extensively studied and applied for computer-aided optimization. Recently there has been a surge of interest in developing algorithms for search, optimization, and communication by simulating different aspects of the social life of a very well-known creature: the honey bee. Several articles reporting the success of the heuristics based on swarming, mating, and foraging behaviors of the honey bees are being published on a regular basis. In this paper we provide a brief but comprehensive survey of the entire horizon of research so far undertaken on the algorithms inspired by the honey bees. Starting with the biological perspectives and motivations, we outline the major bees-inspired algorithms, their prospects in the respective problem domains and their similarities and dissimilarities with the other swarm intelligence algorithms. We also provide an account of the engineering applications of these algorithms. Finally we identify some open research issues and promising application areas for the bees-inspired computing techniques.

https://dr.ntu.edu.sg/bitstream/10356/83447/1/Computing%20with%20the%20collective%20intelligence%20of%20honey%20bees%20%e2%80%93%20A%20survey.pdf

Computing with the collective intelligence of honey bees – A survey

This paper presents a comprehensive survey on application of various conventional, optimization and artificial intelligence (AI) based computational techniques for impact assessment of optimally placed and coordinated control of distributed generations (DGs) and flexible AC transmission systems (FACTS) controllers in power systems (PSs). This review work is presented from the viewpoint of different performance criterion enhancement of PSs such as minimization of active and reactive power losses and reduction of cost of system, more flexible operation and control, increase overall system energy efficiency, increase reliability of system, relieved transmission and distribution congestion, enhance power quality (PQ) of system, improve the voltage profile (VP), increase the loadability of systems, increase available power transfer capacity (APTC) of system, enhance PS stability, reduce PS oscillations, improve environmental GHG conditions and provide the reactive power support in emergency case such as under fault, sudden change in field excitation of alternators or load increase in PSs. This paper also presents the current status of the various conventional, optimization, and AI computational techniques for optimally placed and coordinated control of DG and FACTS controllers in PSs for enhancement of PSs performance parameters. Authors strongly believe that this survey will be very much useful to the researchers, scientific engineers, industrial persons working in this area to find out the relevant references and current state of the art.

A survey on impact assessment of DG and FACTS controllers in power systems

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.

/pdf/a-review-on-artificial-bee-colony-algorithm-1qohr1v61p.pdf

A review on Artificial Bee Colony algorithm

In this paper, a communication strategy for the parallelized Artificial Bee Colony (ABC) optimization is proposed for solving numerical optimization problems. The artificial agents are split into several independent subpopulations based on the original structure of the ABC, and the proposed communication strategy provides the information flow for the agents to communicate in different subpopulations. Three benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental result, the proposed communicational strategy increases the accuracy of the ABC on finding the near best solution.

Ruhai Luo

Papers

Parallelized Artificial Bee Colony with Ripple-communication Strategy