be in the range of [-1, 1]. The value of limit (maximum cycle number) should be SN × D, where, SN is the number of possible solutions and D is the dimension of the problem. Wei-feng Gao et al. [11] proposed an improved solution search method in ABC, which depends on the fact that **bee** searches around the best solution of the preceding iteration to increase the exploitation. A. Banharnsakun et al. [12] introduced a new variant of ABC namely the best-so-far selection in **artificial** **bee** **colony** **algorithm**. To enhance the exploitation and exploration processes, they propose to make three major changes by introducing the best-so-far method, an adjustable search radius, and an objective-value- based comparison method in DE. J.C. Bansal et al. [13] anticipated balanced ABC; they added a new control parameter, Cognitive Learning Factor and also tailored range of ɸ in **Artificial** **Bee** **Colony** **algorithm**. Qingxian and Haijun [14] anticipated a change in the initialization scheme by making the initial group symmetrical, and the Boltzmann selection mechanism was employed instead of roulette wheel selection for humanizing the convergence ability of the ABC **algorithm**.

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Recently, similar to the existing nature inspired algorithms, a new mimic **algorithm** inspired by the behaviors of bees, named **Artificial** **Bee** **Colony** **algorithm**, was proposed by Karaboga and Basturk [8]. In [9], it has been shown that ABC **algorithm** is superior to other optimization algorithms such as GA, PSO and Differential Evolution (DE), etc. Due to the simplicity and robustness of ABC **algorithm**, it has been implemented to solve various problems in image processing, robot path planning, parameter identification, job-shop scheduling.

In this paper, we have highlighted the need of path optimization. Due to increasing time constraints, there is a need to cover the path in the most optimum manner. Where, the path is defined as the trajectory to be followed starting from a specific start position up to a predefined destination or goal point. Further, we have described **Artificial** **Bee** **Colony** **Algorithm**, which is one of the many techniques of Swarm Intelligence and can be successfully applied for optimizing the path. Moreover, a comparison between two Swarm Intelligence techniques, namely **Artificial** **Bee** **Colony** **algorithm** and Ant **Colony** Optimization **algorithm** is presented. It is observed that ants are good in search and exploitation and thus ACO can be used for dynamic applications. However, its theoretical analysis is difficult and the probability distribution changes by iteration. Moreover, the time for convergence is uncertain. ABC on the other hand, employees fewer parameters, it has strong robustness, fast convergence and high flexibility. It can also be used for solving multimodal and multidimensional optimization problems. In addition to this, ABC has global optimization and easy recognition. It conducts both local search and global search in each iteration and as a result the probability of finding the optimal increases. The structure of the ABC **algorithm** is such that it supports parallel processing as result saving time. Considering the wide number of advantages of **Artificial** **Bee** **Colony** **algorithm** (ABC) in comparison to other Swarm intelligence techniques, it can be concluded that ABC is the most efficient **algorithm** for optimizing a given path.

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Emoticon classification is an important step in facial expression recognition. In order to improve the recognition rate. Ensemble Learning method use multiple classifiers to determine the expression category. However, among those classifiers in Ensemble Learning, there are some classifiers that are redundancy and poor performance. In order to solve this problem, **Artificial** **Bee** **Colony** **algorithm** is used to assign different weights to different classifiers, which gives high weight to the classifier performance better, otherwise, it gives low or zero weight to the classifier performance bad. So as to optimize the selection. Experimental results show that this method not only improves the expression recognition rate, but also reduces the computational cost.

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From the experimental results, it clearly shows the ABC based algorithms performs better than the state of art PHUIM algorithms. High utility itemset mining based on **artificial** **bee** **colony** **algorithm** is proposed. The proposed PHUIM-ABC **algorithm** mines 50% faster than the state-of-art algorithms. The candidate itemset generated by the proposed PHUIM-ABC has 97% correct value compared to the FHM **algorithm**. For mushroom data set it performs 50% faster for all the values of minimum utility threshold. The memory usage is reduced by a minimum of 40% for the mushroom, connect and retail datasets. For accident and foodmart the memory usage is reduced by minimum of 20%. Since retail industries need a very fast output than the exact outputs; this **algorithm** will be best suited. The correctness of the **algorithm** is above 80 % for all the dataset and for all the minimum threshold value. In the future it is planned to implement genetic algorithms for PHUIM to improve efficiency. This **algorithm** can be also extended to sequential utility mining algorithms.

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Recently, Cui et al. [5] proposed an **artificial** **bee** **colony** **algorithm** (the ABC elite) with two novel search equations. One search equation incorporates the beneficial infor- mation of elite solutions, which is applied to the employed **bee** phase, the other one not only exploits the valuable information of the elite solutions, but also employs that of the current best solution used in the onlooker **bee** phase. Furthermore, the ABC elite is embedded into depth-first framework to form a new variant of ABC, the DFSABC elite. Experimental results show that ABC-elite and DFSABC elite are very effective compared with other recently proposed ABC variants.

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value of limit (utmost cycle number) should be SN × D, where, SN is the number of solutions and D is the dimension of the problem. W Gao et al. [17] anticipated an enhanced solution search equation in ABC, which is based on the fact that **bee** searches only around the best solution of the previous iteration to increase the exploitation and introduce a selective probability. A. Banharnsakun et al. [18] proposed a novel variant of ABC that is to say the best-so- far selection in **artificial** **bee** **colony** **algorithm**. In this **algorithm** the best possible solutions established so far are shared globally in the midst of the entire population. Thus, the new contender solutions are more plausible to be close to the in progress best solution. In other words, we bias the solution direction toward the best-so-far position. Moreover, every succession adjusts the radius of the search for new individual using a larger radius previously in the search procedure and then reduces the radius as the process comes closer to converging. Finally, it uses a more robust calculation to determine and put side by side the quality of alternative solutions. To enhance the exploitation and exploration processes, they propose to make three major changes by introducing the best-so-far method, an adjustable search radius, and an objective-value-based comparison method in DE. J.C. Bansal et al. [56] wished- for balanced ABC; they introduced a novel control parameter, Cognitive Learning Factor and also modified range of ɸ ij in **Artificial** **Bee** **Colony** **algorithm**.

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The integration of optimization methods in the different processes involved in an electric power system in the search for energetic efficiency has generated satisfying results in the reduction of energy consumption, technical losses, increasing security and system reliability. The purpose of this article is to implement the **artificial** **bee** **colony** optimization **algorithm** in a 15-node IEEE power system set at 13.2 kV, in order to find the possible values of the reactive compensation that optimize the system power flow. In first place, the results of the voltage profiles of a 15-node IEEE power distribution system are shown with the Newton Raphson method. Then, said system is optimized using an adapted version of the **artificial** **bee** **colony** **algorithm** which was developed in MATLAB. After the execution of the **algorithm**, it was concluded that the nodal voltage values have a significant increase in all 15 nodes of the system. This translates into a reduction of the losses in the interconnection lines of the nodes through the optimization of the power system. The application of the **artificial** **bee** **colony** **algorithm** offers an optimization alternative driven to reduce the energy losses in the power system. Keywords: **algorithm**, optimization, power flow, power system.

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I N the recent past, researchers have shown interest in algorithms inspired from natural phenomena. To name a few we have Particle Swarm Optimisation (PSO) **algorithm** [23] taking inspiration from birds flocking, **Artificial** **Bee** **Colony** (ABC) optimization **algorithm** [21] inspired by foraging behaviour of honey bees, Gravitational Search **Algorithm** (GSA) [28] taking inspiration from law of gravity and interaction between the masses, Harmony Search **Algorithm** (HSA) [12] inspired by improvisation done by jazz musician, Differential Evolution (DE) **algorithm** [30] inspired by theory of evolution and Spider Monkey Optimization (SMO) [4] **algorithm** taking inspiration from foraging behaviour of spider monkeys. Recently various variants of ABC **algorithm** have been proposed which includes modified global best **artificial** **bee** **colony** for constrained optimization problem [3], **artificial** **bee** **colony** **algorithm** with multiple search strategies [11], an adaptive **artificial** **bee** **colony** **algorithm** for global optimization [35], hybrid **artificial** **bee** **colony** with differential evolution [18][34], simulated annealing based **artificial** **bee** **colony** **algorithm** for global numerical optimization [6] and escalated convergent **artificial** **bee** **colony** [17]. Study has shown that these algorithms are considered as an efficient solver of complex optimization problems. **Artificial** **Bee** **Colony** (ABC) optimization **algorithm** and its variants has been applied to various optimization problems such as solving partition and scheduling problem in codesign [24][14], **artificial** neural networks [25], forecasting stock markets [15], automatic software fault localization [16], parameter identification for Van Der Pol - Duffing oscillator [10], network topology design [29] and structural engineering [8].

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It is observed that a great number of function eval- uations is needed for **artificial** **bee** **colony** **algorithm** to obtain a satisfactory solution. Accordingly, a great num- ber of cycles or generations is evolved during optimization process. It is well known that population diversity in EAs and SI algorithms heavily reduces once algorithms are evolved in a large number of generations. This means solutions tend to be homogeneous or most genetic infor- mation becomes identical. Thus, such algorithms could not produce diverse variations and then lose the capabil- ity of refining fitness of solutions. In ABC, scout bees is responsible to diversify solutions for having a wider search area. However, recent study shows that this method is not effective to speed up convergence rate of ABC [27]. In this paper, a non-revisiting scheme is used to keep population diversity substituting for scout **bee** stage in ABC. The idea of non-revisiting scheme is to restrict an **algorithm** from revisiting an already searched place. For one thing, it avoids revisiting and repeated solution evaluation. For another thing, it memorizes all visited places by **algorithm** so as to let the search of **algorithm** focus on unknown places with high uncertainty. Besides non-revisiting scheme, three **bee** groups in ABC are also changed. As non-revisiting scheme is apt to guide the search directions of an **algorithm**, employed and scout bees are eliminated from standard ABC **algorithm**. Hence, only onlooker **bee** stage is kept in the resulting algo- rithm. The new **algorithm** is named as non-revisiting **artificial** **bee** **colony** (NrABC). NrABC is then applied to tackle phased array design problems. Numerical experi- ment is conducted studying NrABC and standard ABC. The results are discussed and analyzed at the end of the paper.

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function, we trained the network both traditional **Artificial** **Bee** **Colony** **algorithm** and a variant of ABC proposed by us. Proposed **algorithm** uses dynamically constructed hy- persphere to generate new **bee** population. The individuals in new population fall into the hypersphere to increase the exploitation ability of traditional ABC. Similarly, the individuals generated outside of the hypersphere supports the exploration quality of ABC. In this work, we give some numerical examples some different types of differential equa- tions such as first order and second order ODEs. The empirical studies precisely clarify that the modified version of ABC outperforms the classical ABC by means of absolute and mean squared errors. Table 6 exposes that cost values obtained in training and the testing stages are quite similar. Only, the desired improvement has not been achieved for the second order differential equation. Furthermore, it can be observed that the improvement has been drastic at the initial steps of the **algorithm** with Figure 2. However, it has been slight for following steps of the proposed **algorithm**.

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Since invention of ABC 2005, studies on ABC in the literature have increased significantly. The ABC was used for designing of digital IRR filters by Karaboga [7], Singh used it for leaf-constrained minimum spanning tree problem [8], Rao et al. proposed ABC for optimization of distribution network configuration for loss reduc- tion [9]. The ABC was implemented to solve quadratic minimum spanning tree problem by Sundar and Singh [10]. A modified ABC for real parameter optimization was proposed by Akay and Karaboga [11]. Karaboga and Akay modified ABC for solving constrained optimization problems by using Deb’s rules [12]. Pan et al. devel- oped a discrete model of ABC for lot-streaming ﬂow shop scheduling problem [13]. The ABC was also used for solving reliability redundancy allocation problems [14], neural networks training [15], software test suite opti- mization [16]. In addition, Gbest-guided ABC for numerical function optimization was proposed by Zhu and Kwong [17] and Alatas proposed a chaotic **artificial** **bee** **colony** **algorithm** for avoiding to get stuck on local so- lutions [18].

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Thus, it is concluded that MAP-ABC **algorithm** minimizes the localization error better than MAP-M&N. Hybridization of optimization namely Simulated Annealing (SA) can be combined to Mobile Anchor Positioning with **Artificial** **Bee** **Colony** **algorithm** (MAP-ABC-SA) so as to reduce the localization error further and the localization error of the hybrid evolutionary **algorithm** can be compared with the pure ABC **algorithm** (MAP-ABC) to validate its performance. The future enhancement may be applying meta-heuristic optimization approaches such as Glow worm swarm optimization, fish swarm optimization etc. with mobile anchor positioning to further minimize the localization error significantly in wireless sensor networks.

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Recently, many swarm-bsed algorithms have been proposed, including genetic **algorithm** (GA) [1], particle swarm optimization **algorithm** (PSO) [2], ant **colony** optimization **algorithm** (ACO) [3], differential evolution **algorithm** (DE) [4], harmony search **algorithm** (HS) [5], **artificial** **bee** **colony** **algorithm** (ABC) [6]. ABC proposed by Karaboga is one of the most popular swarm-based algorithms, which is based on the intelligent foraging behavior of honey **bee** swarm. For the reason that it is simple and easy to implement, ABC **algorithm** has attracted a lot of scholars’ attention. Benchmark functions experiment has shown that ABC is competitive over GA, DE and PSO **algorithm**. Since proposed, ABC has been widely used in optimization problems.

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Abstract— Data science is all about performing various operations on various type of data. Big data is a large amount of data which is hard to handle by on hand systems. It requires new structures, algorithms and techniques. As data increases as per volume, dark data also will increase. **Artificial** **Bee** **Colony** **algorithm** is a part of Swarm Intelligence. It is based on how honey bees are working to find out their food sources. In Big Data there is distributed environment so required sources may be on different places. During process the data these data sources have to find out from different places and analyze a one system. This requires calculation which can help us to find out best option for our required data sources. ABC **algorithm** is used to overcome limitations of ant **colony** **algorithm**. In ant **colony** initialization will be repeat from starting point in case of failure. In **bee** **colony** optimization initialization happens only once. It is used to find out required data source based on parameters out of multiple data sources. Thus, **artificial** **bee** **colony** **algorithm** can be used to find out best data sources. We can store these derived data sources on cloud for further processing. **Bee** **colony** **algorithm** generally used in data mining and networking field. It can be used for Big Data for identifying data resources.

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[Chidambaram and Lopes (2009)] proposed a new method which applied the **Artificial** **Bee** **Colony** **Algorithm** (ABC) to recognize objects in the images. The objective was to find a pattern or reference image (template) of an object somewhere in a target landscape scene. Considering that it may be translated, scaled, rotated and/or partially occluded to identify th e given reference image in the target landscape image. The best solutions obtained in their experiments with gray scale and color images indicated that ABC **algorithm** was much faster than the other evolutionary algorithms and with comparable accuracy. They also claimed that to the best of their knowledge, this was the first application of the ABC **algorithm** to this sort of problem.

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Nature Inspired Algorithms (NIAs) mimics the intelligent behaviour of social insects like bees, ants, termites, fish and birds etc. Swarm Intelligence getting popularity now days and become a rising and fascinating area. It depends on the cooperative behaviour of societal living thing. Societal individual make use of their skill of societal wisdom to crack multifaceted everyday jobs. The main power of swarm based optimization strategy is multiple interactions in societal colonies. Swarm intelligence strategies have potential to solve complex factual world optimization problems as the preceding study [1, 2, 3, 4] have exposed. The ant **colony** optimization (ACO) [1], bacterial foraging optimization (BFO) [5], particle swarm optimization (PSO) [2] and **artificial** **bee** **colony** **algorithm** [6] are some of popular algorithms that have surfaced in recent years.

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The **Artificial** **Bee** **Colony** (ABC) **algorithm** is a new swarm intelligence technique inspired by intelligent foraging behavior of honey bees. The first framework of ABC **algorithm** mimicking the foraging behavior of honey **bee** swarm in finding good solutions to optimize multi-variable and multi-modal continuous functions was presented by D.Karaboga. Numerical comparisons demonstrated that the performance of the ABC **algorithm** is competitive to other population-based **algorithm** with an advantage of employing fewer control parameters [1], [2], [3]. The basic version of the **Artificial** **Bee** **Colony** **algorithm** has only one control parameter ‘‘ limit ” apart from the common control parameters of the population- based algorithms such as population size or **colony** size (SN) and maximum generation number or maximum cycle number (MCN).Due to its simplicity and ease of implementation, the ABC **algorithm** has captured much attention and has been applied to solve many practical optimization problems. Singh [4] used the ABC **algorithm** for the leaf-constrained minimum spanning tree problem and compared the approach against genetic **algorithm** (GA), ant **colony** optimization (ACO) and tabu search (TS). Pan et.al [5] proposed a discrete ABC **algorithm** in combinational optimization for flow shop scheduling problem. Feng et.al [6] proposed a hybrid simplex **artificial** **bee** **colony** **algorithm** which combines Nelder-Mead simplex method to solve inverse analysis problems. Rao et al. [7] applied the ABC **algorithm** to network reconfiguration problem in a radial distribution

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