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

This paper reviews studies using empirical, data-driven methodologies, such as cluster analysis and latent class analysis, to identify clustering patterns of diet, physical activity and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity.

The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review

Fundamentals of statistical signal processing: Estimation theory: by Steven M. KAY; Prentice Hall signal processing series; Prentice Hall; Englewood Cliffs, NJ, USA; 1993; xii + 595 pp.; $65; ISBN: 0-13-345711-7

Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms' performances.

A quick artificial bee colony (qABC) algorithm and its performance on optimization problems

A survey on applications and variants of the cuckoo search algorithm

Wireless sensor networks represent one of the most crucial components of novel technologies, such as the internet of things and cloud computing. One of the greatest problems in any wireless sensor network is lifetime maximization, and it could be tackled by decreasing energy consumption by the nodes Numerous clustering algorithms have emerged in recent years, with the sole goal to establish energy consumption equilibrium among nodes and to increase its efficiency—this term is known as the load balancing. The LEACH, as one of the most important load balancing algorithms is not able to establish satisfying performance and it could be enhanced by using metaheuristics approaches. The research described in this paper has proposed a refined version of the dragonfly algorithm, which is later applied to enhance the lifetime of wireless sensor network. The performance of the proposed enhanced dragonfly metaheuristics has been assessed by comparing with its original implementation, traditional version of the LEACH algorithm, and the particle swarm optimization. Simulation results indicate that our proposed algorithm has superior performances and that it could retrieve valuable results in this domain.

Enhanced Dragonfly Algorithm Adapted for Wireless Sensor Network Lifetime Optimization

This paper introduces implementation of hybridized bat algorithm for multi-objective radio frequency identification network planning problem. Multi-objective RFID problem is a well known hard optimization problem that can be solved by using swarm intelligence algorithms. Bat algorithm is a recent mataheuristic, proved to be very successful for tackling such tasks. In our implementation, we hybridized bat algorithm with the artificial bee colony algorithm and adapted it for solving radio frequency identification network planning problem. In the experimental section, we have first shown, by using standard bound-constrained benchmark functions, that our hybridization is justified and that it improves results compared to standard bat algorithm, as well as to other state-of-the-art algorithms. After that, we examined performance of our proposed approach on illustrative RFID network planning problem and compared it with other results from the literature where our proposed algorithm proved to be more successful.

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

Cloud computing is a relatively new computing technology, which provides online on-demand computing services to cloud users. Task scheduling plays a crucial role in the cloud model. An efficient task allocation method, results with better resource utilization, have an impact on the quality of service, the overall performance, and user experience. The task scheduling should be carried out on multiple criteria, which is a difficult optimization problem and belongs to the class of NP-hard optimization problem. As the complexity of the problem increases, the exhaustive search becomes enormous. Consequently, an optimization technique is needed that can find the approximate solution in less amount of time. In this paper, we propose a hybridized bat optimization algorithm for multi-objective task scheduling. The simulations are performed in the CloudSim toolkit using standard parallel workloads, and the obtained results show that the proposed technique gives better results than other similar methods.

Multi-objective Task Scheduling in Cloud Computing Environment by Hybridized Bat Algorithm

 Abstract—Original Karaboga's artificial bee colony (ABC) algorithm was applicable to unconstrained problems only and modifications for constrained problems were introduced later. In this article we propose an improved artificial bee colony algorithm for constrained problems. Since the ABC algorithm for constrained problems does not consider the initial population to be feasible we introduced a modification, besides penalty function and Deb's rule, in a form of ―smart bee‖ (SB) which uses its historical memories for the location and quality of food sources. This modified SB-ABC algorithm was tested on standard benchmark functions for constrained optimization problems and proved to be better. Keywords—Artificial bee colony, Constrained optimization, Nature inspired metaheuristic algorithms, Swarm intelligence.

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Modified artificial bee colony algorithm for constrained problems optimization

Artificial neural networks, especially deep neural networks, are the promising and current research domain as they showed great potential in classification and regression tasks. The process of training artificial neural network (weight optimization), as an NP-hard challenge, is typically performed by back-propagation algorithms such as stochastic gradient descent. However, these types of algorithms are susceptible to trapping the local optimum. Recent studies show that, the metaheuristics-based approaches like swarm intelligence can be efficiently utilized in training the artificial neural network. This paper presents an improved version of swarm intelligence and monarch butterfly optimization algorithm for training the feed-forward artificial neural network. Since the basic monarch butterfly optimization suffers from some deficiencies, improved implementation, that enhances exploration ability and intensification–diversification balance, is devised. Proposed method is validated against 8 well-known classification datasets and compared to similar approaches that were tested within the same environment and simulation setup. Obtained results indicate that, the method proposed in this work outperforms other state-of-the-art algorithms that are shown in the recent outstanding computer science literature.

Nebojsa Bacanin

Papers

Enhanced Dragonfly Algorithm Adapted for Wireless Sensor Network Lifetime Optimization

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

Multi-objective Task Scheduling in Cloud Computing Environment by Hybridized Bat Algorithm

Modified artificial bee colony algorithm for constrained problems optimization

Weight Optimization in Artificial Neural Network Training by Improved Monarch Butterfly Algorithm