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

This paper presents modified moth search algorithm for solving global optimization problems. Moth search algorithm is novel swarm intelligence metaheuristics. By analyzing original moth search approach, we noticed some deficiencies in the search process of subpopulation 2. Modified moth search addresses these weaknesses. To prove the robustness of our approach we tested our algorithm on six standard global optimization benchamarks and performed comparative analysis with original moth search, as well as with other five state-of the-art metaheuristics. Testing results show that in average modified moth search outperforms other approaches included in comparative analysis.

Modified Moth Search Algorithm for Global Optimization Problems

Cloud computing is rather important distributing computing paradigm and in general refers to the common pool of configurable resources that is accessed on-demand. Resources are dynamically scalable and metered with the basic aim to provide reliable and quality services to the end-users. Load scheduling has a great impact on the overall performance of the cloud system, and at the same time it is one of the most challenging problems in this domain. In this paper, we propose implementation of the hybridized elephant herding optimization applied to load scheduling problem in cloud computing. The algorithm is using CloudSim framework, and comparison with different metaheuristics, adapted and tested under same experimental conditions, for this type of problem was performed. Moreover, we compared proposed hybridized elephant herding optimization with its original version in order to evaluate its improvements in performance over the original version. Obtained empirical results prove the robustness and quality of approach that we propose in this paper.

Hybrid Elephant Herding Optimization Approach for Cloud Computing Load Scheduling

As solar energy generation has become more and more important for the economies of numerous countries in the last couple of decades, it is highly important to build accurate models for forecasting the amount of green energy that will be produced. Numerous recurrent deep learning approaches, mainly based on long short-term memory (LSTM), are proposed for dealing with such problems, but the most accurate models may differ from one test case to another with respect to architecture and hyperparameters. In the current study, the use of an LSTM and a bidirectional LSTM (BiLSTM) is proposed for dealing with a data collection that, besides the time series values denoting the solar energy generation, also comprises corresponding information about the weather. The proposed research additionally endows the models with hyperparameter tuning by means of an enhanced version of a recently proposed metaheuristic, the reptile search algorithm (RSA). The output of the proposed tuned recurrent neural network models is compared to the ones of several other state-of-the-art metaheuristic optimization approaches that are applied for the same task, using the same experimental setup, and the obtained results indicate the proposed approach as the better alternative. Moreover, the best recurrent model achieved the best results with R2 of 0.604, and a normalized MSE value of 0.014, which yields an improvement of around 13% over traditional machine learning models.

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Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation

This work considers the problem of target localization in a 3-D space, using coupled measurements of angle of arrival (AoA) and received signal strength (RSS). By applying the transformation from Cartesian to spherical coordinates and by taking advantage of the available AoA observations, we establish novel relationships between the measurements and the unknown target location. From these relationships, we derive a simple closed-form solution estimator. Simulation results validate the remarkable performance of the proposed estimator, both in terms of estimation accuracy and execution time.

An efficient WLS estimator for target localization in wireless sensor networks

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. 
Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). 
Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. 
Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior

Nebojsa Bacanin

Papers

Modified Moth Search Algorithm for Global Optimization Problems

Hybrid Elephant Herding Optimization Approach for Cloud Computing Load Scheduling

Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation

An efficient WLS estimator for target localization in wireless sensor networks

A New K means Grey Wolf Algorithm for Engineering Problems