A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

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

The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.

Optimizing connection weights in neural networks using the whale optimization algorithm

The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters. That is, we propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. Comparisons between the proposed U-k-means and other existing methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.

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Unsupervised K-Means Clustering Algorithm

A survey on new generation metaheuristic algorithms

Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the foraging behaviour of real ant colonies. The ants deposit pheromone on the ground in order to mark the route for identification of their routes from the nest to food that should be followed by other members of the colony. This ACO exploits an optimization mechanism for solving discrete optimization problems in various engineering domain. From the early nineties, when the first Ant Colony Optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. This paper review varies recent research and implementation of ACO, and proposed a modified ACO model which is applied for network routing problem and compared with existing traditional routing algorithms.

Review: A survey: Ant Colony Optimization based recent research and implementation on several engineering domain

With increasing crime rates in today's world, there is a corresponding awareness for the necessity of detecting abnormal activity. Automation of abnormal Human behavior analysis can play a significant role in security by decreasing the time taken to thwart unwanted events and picking them up during the suspicion stage itself. With advances in technology, surveillance systems can become more automated than manual. Human Behavior Analysis although crucial, is highly challenging. Tracking and recognizing objects and human motion from surveillance videos, followed by automatic summarization of its content has become a hot topic of research. Many researchers have contributed to the field of automated video surveillance through detection, classification and tracking algorithms. Earlier research work is insufficient for comprehensive analysis of human behavior. With the introduction of semantics, the context of a surveillance domain may be established. Such semantics may extend surveillance systems to perform event-based behavior analysis relevant to the domain. This paper presents a survey on research on human behavior analysis with a scope of analyzing the capabilities of the state-of-art methodologies with special focus on semantically enhanced analysis.

Automated human behavior analysis from surveillance videos: a survey

Data Clustering is a technique of finding similar characteristics among the data set which are always hidden in nature and grouping them into groups, called as clusters. Different clustering algorithms exhibit different results, since they are very sensitive to the characteristics of original data set especially noise and dimension. The quality of such clustering process determines the purity of cluster and hence it is very important to evaluate the results of the clustering algorithm. Due to this, Cluster validation activity had been a major and challenging task. The major factor which influences cluster validation is the internal cluster validity measure of choosing the optimal number of clusters. The main objective of this article is to present a detailed description of the mathematical working of few cluster validity indices and not all, to classify these indices and to explore the ideas for the future promotion of the work in the domain of cluster validation. In addition to this, a maximization objective function is defined assuming to provide a cluster validation activity.

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A Survey on Internal Validity Measure for Cluster Validation

Multi-agent systems (MAS) based computing is the most appropriate paradigm for the problem domain, where data, control, expertise or resources are distributed and also it is interesting to the user only if the technologies address the issues of interest to the user. The MAS has the hypothesis that the agent based computing offers better approach to manage the complex systems and process. They are large-scale systems and collaborate with one another to achieve their functions in a highly modular and flexible way. In this point of view, the work presented in this paper is an enhanced attempt to validate the MAS based on application perspective. As a test-bed, a distributed MAS for software testing is constructed such that to provide a hybrid testing environment based on variety of agents, which possibly incorporate several testing techniques. The developed framework is validated on two perspectives namely, efficiency of the application domain, i.e. software testing using MAS and efficiency of the proposed framework. The validation of the later case has been carried out on two conditions: regular working environment and exceptional working environment. The second type of validation provided the guidelines for implementing proper exception handling mechanism in the enhanced MAS, which is being developed for software testing Purpose.

An application perspective evaluation of multi-agent system in versatile environments

"An ounce of prevention is worth a pound of cure." In software, these expressions translate into the common observation that the longer a defect stays in process, the more expensive it is to fix [10]. Moreover software defects are expensive and time consuming. The cost of finding and correcting defects represents one of the most expensive software development activities. And that too, if the errors get carried away till the final acceptance testing stage of the project life cycle, then the project is at a greater risk in terms of its Time and Cost factors. A small amount of effort spent on quality assurance will see good amount of cost savings in terms of detecting and eliminating the defects. To gain a deeper understanding of the effectiveness of the software process, it is essential to examine the details of defects detected in the past projects and to study how the same can be eliminated due to process improvements and newer methodologies. This paper will focus on finding the total number of defects that has occurred in the software development process for five similar projects and aims at classifying various defects using first level of Orthogonal Defect Classification (ODC), finding root causes of the defects and use the learning of the projects as preventive ideas. The paper also showcases on how the preventive ideas are implemented in a new set of projects resulting in the reduction of the number of similar defects.

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Ramachandran Baskaran

Papers

Review: A survey: Ant Colony Optimization based recent research and implementation on several engineering domain

Automated human behavior analysis from surveillance videos: a survey

A Survey on Internal Validity Measure for Cluster Validation

An application perspective evaluation of multi-agent system in versatile environments

Defect Analysis and Prevention for Software Process Quality Improvement