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

The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

This paper presents implementation of the moth search algorithm adjusted for solving static drone location problem. The optimal location of drones is one of the most important issues in this domain, and it belongs to the group of NP-hard optimization. The objective of the model applied in this paper is to establish monitoring all targets with the least possible number of drones. For testing purposes, we used problem instance with 30 uniformly distributed targets in the network domain. According to the results of simulations, where moth search algorithm established full coverage of targets, this approach shows potential in dealing with this kind of problem.

Moth Search Algorithm for Drone Placement Problem

This paper presents dynamic version of the tree growth algorithm. Tree growth algorithm is a novel optimization approach that belongs to the group of swarm intelligence metaheuristics. Only few papers addressed this method so far. This algorithm simulates the competition between the trees for resources such as food and light. The dynamic version of the tree growth algorithm introduces dynamical adjustment of exploitation and exploration search parameters. The efficiency and robustness of the proposed method were tested on a well-known set of standard global unconstrained benchmarks. Besides numerical results obtained by dynamic tree growth algorithm, in the experimental part of this paper, we have also shown comparative analysis with the original tree growth algorithm, as well as comparison with other methods, which were tested on the same benchmark set. Since many problems from the domains of industrial and service systems can be modeled as global optimization tasks, dynamic tree growth algorithm shows great potential in this area and can be further adapted for tackling many real-world unconstrained and constrained optimization challenges.

Dynamic Search Tree Growth Algorithm for Global Optimization

This paper presents the application of the hybridized bat algorithm to the constrained portfolio optimization problem which is a hard optimization problem suitable for stochastic optimization metaheuristics. Bat algorithm is a recent member of the group of nature-inspired algorithms. Hybridization between bat and artificial bee colony metaheuristics was adapted for solving portfolio problem with constraints that extend classical mean-variance portfolio selection formulation. To test the robustness of our hybridized approach, a comparative analysis with other swarm intelligence algorithms, as well as with three variants of genetic algorithm was performed. All algorithms included in comparative analysis were tested on the same portfolio model using the same data set. Results show that proposed hybridized bat algorithm has a great potential for tackling constrained portfolio problem.

Constrained Portfolio Optimization by Hybridized Bat Algorithm

https://recil.grupolusofona.pt/bitstream/10437/9738/1/PhyCom_Published_Version_November_2017.pdf

Nebojsa Bacanin

Papers

Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

Moth Search Algorithm for Drone Placement Problem

Dynamic Search Tree Growth Algorithm for Global Optimization

Constrained Portfolio Optimization by Hybridized Bat Algorithm

Bayesian methodology for target tracking using combined RSS and AoA measurements