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Showing papers on "Swarm intelligence published in 2020"


Journal ArticleDOI
TL;DR: A novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows, which shows that the proposed SSA is superior over GWO, PSO and GSA in terms of accuracy, convergence speed, stability and robustness.
Abstract: In this paper, a novel swarm optimization approach, namely sparrow search algorithm (SSA), is proposed inspired by the group wisdom, foraging and anti-predation behaviours of sparrows. Experiments ...

1,114 citations


Journal ArticleDOI
TL;DR: The processes of nuptial dance and random flight enhance the balance between algorithm’s exploration and exploitation properties and assist its escape from local optima.

356 citations


Journal ArticleDOI
TL;DR: A survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence can be found in this paper, along with a description of the statistical background of these tests.
Abstract: A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.

255 citations


Journal ArticleDOI
TL;DR: This paper aims to undertake a comprehensive review on meta-heuristic algorithms and related variants which have been applied on PV cell parameter identification and presents some perspectives and recommendations for future development.

212 citations


Journal ArticleDOI
02 Apr 2020
TL;DR: This paper collects and categorizes swarm behaviors into spatial organization, navigation, decision making, and miscellaneous and gives a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market.
Abstract: In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

209 citations


Journal ArticleDOI
TL;DR: Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed.
Abstract: Grey Wolf Optimizer (GWO) is a new swarm intelligence algorithm mimicking the behaviours of grey wolves. Its abilities include fast convergence, simplicity and easy realization. It has been proved its superior performance and widely used to optimize the continuous applications, such as, cluster analysis, engineering problem, training neural network and etc. However, there are still some binary problems to optimize in the real world. Since binary can only be taken from values of 0 or 1, the standard GWO is not suitable for the problems of discretization. Binary Grey Wolf Optimizer (BGWO) extends the application of the GWO algorithm and is applied to binary optimization issues. In the position updating equations of BGWO, the a parameter controls the values of A and D , and influences algorithmic exploration and exploitation. This paper analyses the range of values of A D under binary condition and proposes a new updating equation for the a parameter to balance the abilities of global search and local search. Transfer function is an important part of BGWO, which is essential for mapping the continuous value to binary one. This paper includes five transfer functions and focuses on improving their solution quality. Through verifying the benchmark functions, the advanced binary GWO is superior to the original BGWO in the optimality, time consumption and convergence speed. It successfully implements feature selection in the UCI datasets and acquires low classification errors with few features.

204 citations


Journal ArticleDOI
TL;DR: A comprehensive survey on the state-of-the-art works applying swarm intelligence to achieve feature selection in classification, with a focus on the representation and search mechanisms.
Abstract: One of the major problems in Big Data is a large number of features or dimensions, which causes the issue of “the curse of dimensionality” when applying machine learning, especially classification algorithms. Feature selection is an important technique which selects small and informative feature subsets to improve the learning performance. Feature selection is not an easy task due to its large and complex search space. Recently, swarm intelligence techniques have gained much attention from the feature selection community because of their simplicity and potential global search ability. However, there has been no comprehensive surveys on swarm intelligence for feature selection in classification which is the most widely investigated area in feature selection. Only a few short surveys is this area are still lack of in-depth discussions on the state-of-the-art methods, and the strengths and limitations of existing methods, particularly in terms of the representation and search mechanisms, which are two key components in adapting swarm intelligence to address feature selection problems. This paper presents a comprehensive survey on the state-of-the-art works applying swarm intelligence to achieve feature selection in classification, with a focus on the representation and search mechanisms. The expectation is to present an overview of different kinds of state-of-the-art approaches together with their advantages and disadvantages, encourage researchers to investigate more advanced methods, provide practitioners guidances for choosing the appropriate methods to be used in real-world scenarios, and discuss potential limitations and issues for future research.

202 citations


Journal ArticleDOI
TL;DR: A survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence is conducted and a description of the statistical background of these tests is included.
Abstract: A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.

196 citations


Journal ArticleDOI
TL;DR: In this paper, the authors provide an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations, identifying some challenging issues and five open problems concerning the analysis of algorithmic convergence and stability.
Abstract: Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.

163 citations


Journal ArticleDOI
TL;DR: A new metaheuristic algorithm called group teaching optimization algorithm (GTOA) is presented, inspired by group teaching mechanism, which needs only the essential population size and stopping criterion without extra control parameters and has great potential to be used widely.
Abstract: In last 30 years, many metaheuristic algorithms have been developed to solve optimization problems. However, most existing metaheuristic algorithms have extra control parameters except the essential population size and stopping criterion. Considering different characteristics of different optimization problems, how to adjust these extra control parameters is a great challenge for these algorithms in solving different optimization problems. In order to address this challenge, a new metaheuristic algorithm called group teaching optimization algorithm (GTOA) is presented in this paper. The proposed GTOA is inspired by group teaching mechanism. To adapt group teaching to be suitable for using as an optimization technique, without loss of generality, four simple rules are first defined. Then a group teaching model is built under the guide of the four rules, which consists of teacher allocation phase, ability grouping phase, teacher phase and student phase. Note that GTOA needs only the essential population size and stopping criterion without extra control parameters, which has great potential to be used widely. GTOA is first examined over 28 well-known unconstrained benchmark problems and the optimization results are compared with nine state-of-the-art algorithms. Experimental results show the superior performance of the proposed GTOA for these problems in terms of solution quality, convergence speed and stability. Furthermore, GTOA is used to solve four constrained engineering design optimization problems in the real world. Simulation results demonstrate the proposed GTOA can find better solutions with faster speed compared with the reported optimizers.

128 citations


Journal ArticleDOI
TL;DR: It is suggested that proposed models are more robust than the classifiers, which were used for benchmarking and they are good alternatives for flood susceptibility mapping given the availability of dataset.

Proceedings ArticleDOI
22 Oct 2020
TL;DR: Considering the features such as simplicity, flexibility, ability to search randomly and avoiding local optima, a new control algorithm whose KP, KI, and KD parameter values optimized by HHO, have been proposed for UAV’s attitude and altitude control are proposed.
Abstract: Nowadays, it is very important for the success of the determined missions or operations that the Unmanned Aerial Vehicles (UAVs), which are used extensively in the performance of many civil and military tasks, follow the predetermined path with high accuracy at the determined altitude. The fact that the UAV performs its mission by adhering to the predetermined height and path enables the UAV to spend less energy and therefore fly for a longer time. Many traditional control algorithms, especially Proportional-Integral-Derivative (PID), are used in the attitude and altitude control of UAV for path following. Unlike other studies, in this study, metaheuristic optimization algorithms based on swarm intelligence estimate the parameters of the control algorithm proposed for UAV. Using meta-heuristic optimization algorithms such as Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO), both attitude and altitude control of the quadrotor have been performed for path following in routes with different geometries such as rectangle, circle, and lemniscate. The performance of each control algorithm in the study for the specified routes has been tested and the test results obtained have been compared with each other. Considering the features such as simplicity, flexibility, ability to search randomly and avoiding local optima, a new control algorithm whose K P , K I , and K D parameter values optimized by HHO, have been proposed for UAV’s attitude and altitude control.

Journal ArticleDOI
01 Jan 2020
TL;DR: An effective variant of SMO to solve TSP called discrete SMO (DSMO), where every spider monkey represents a TSP solution where Swap Sequence and Swap Operator based operations are employed, which enables interaction among monkeys in obtaining the optimal T SP solution.
Abstract: Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Travelling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behaviour of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

Journal ArticleDOI
TL;DR: A comprehensive survey of the most recent approaches involving the hybridization of SI and EC algorithms for DL, the architecture of DNNs, and DNN training to improve the classification accuracy is presented.
Abstract: Deep learning (DL) has become an important machine learning approach that has been widely successful in many applications. Currently, DL is one of the best methods of extracting knowledge from large sets of raw data in a (nearly) self-organized manner. The technical design of DL depends on the feed-forward information flow principle of artificial neural networks with multiple layers of hidden neurons, which form deep neural networks (DNNs). DNNs have various architectures and parameters and are often developed for specific applications. However, the training process of DNNs can be prolonged based on the application and training set size (Gong et al. 2015). Moreover, finding the most accurate and efficient architecture of a deep learning system in a reasonable time is a potential difficulty associated with this approach. Swarm intelligence (SI) and evolutionary computing (EC) techniques represent simulation-driven non-convex optimization frameworks with few assumptions based on objective functions. These methods are flexible and have been proven effective in many applications; therefore, they can be used to improve DL by optimizing the applied learning models. This paper presents a comprehensive survey of the most recent approaches involving the hybridization of SI and EC algorithms for DL, the architecture of DNNs, and DNN training to improve the classification accuracy. The paper reviews the significant roles of SI and EC in optimizing the hyper-parameters and architectures of a DL system in context to large scale data analytics. Finally, we identify some open problems for further research, as well as potential issues related to DL that require improvements, and an extensive bibliography of the pertinent research is presented.

Journal ArticleDOI
28 Mar 2020-Sensors
TL;DR: Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios.
Abstract: Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called modified aging ant colony optimization (AACO). The AACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

Journal ArticleDOI
TL;DR: The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has been applied to deal with the accurate resource allocation between the end users in the way of creating the cost mapping tables creations and optimal allocation in MEC.

Journal ArticleDOI
TL;DR: This study encourages the researchers and developers of meta-heuristic algorithms to use symbiotic organisms search (SOS), which has been able to solve the majority of engineering issues so far, because it is a simple and powerful algorithm to solve complex and NP-hard problems.
Abstract: Recently, meta-heuristic algorithms have made remarkable progress in solving types of complex and NP-hard problems. So that, most of this algorithms are inspired by swarm intelligence and biological systems as well as other physical and chemical systems in nature. Of course, different divisions for meta-heuristic algorithms have been presented so far, and the number of these algorithms is increasing day by day. Among the meta-heuristic algorithms, some algorithms have a very high efficiency, which are a suitable method for solving real-world problems, but some algorithms have not been sufficiently studied. One of the nature-inspired meta-heuristic algorithms is symbiotic organisms search (SOS), which has been able to solve the majority of engineering issues so far. In this paper, firstly, the primary principles, the basic concepts, and mathematical relations of the SOS algorithm are presented and then the engineering applications of the SOS algorithm and published researches in different applications are examined as well as types of modified and multi-objective versions and hybridized discrete models of this algorithm are studied. This study encourages the researchers and developers of meta-heuristic algorithms to use this algorithm for solving various problems, because it is a simple and powerful algorithm to solve complex and NP-hard problems. In addition, a detailed and perfect statistical analysis was performed on the studies that had used this algorithm. According to the accomplished studies and investigations, features and factors of this algorithm are better than other meta-heuristic algorithm, which has increased its usability in various fields.

Journal ArticleDOI
21 Mar 2020-Entropy
TL;DR: A systematic literature review about variants and improvements of the Particle Swarm Optimisation algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones is made.
Abstract: The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space seeking for the global best position that minimises (or maximises) a given problem. Today, PSO is one of the most well-known and widely used swarm intelligence algorithms and metaheuristic techniques, because of its simplicity and ability to be used in a wide range of applications. However, in-depth studies of the algorithm have led to the detection and identification of a number of problems with it, especially convergence problems and performance issues. Consequently, a myriad of variants, enhancements and extensions to the original version of the algorithm, developed and introduced in the mid-1990s, have been proposed, especially in the last two decades. In this article, a systematic literature review about those variants and improvements is made, which also covers the hybridisation and parallelisation of the algorithm and its extensions to other classes of optimisation problems, taking into consideration the most important ones. These approaches and improvements are appropriately summarised, organised and presented, in order to allow and facilitate the identification of the most appropriate PSO variant for a particular application.

Journal ArticleDOI
TL;DR: A comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems.
Abstract: In this article, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

Journal ArticleDOI
TL;DR: The proposed IMEHO method uses a global velocity strategy and a novel learning strategy to update the velocity and position of the individuals and an elitism strategy is also adopted to ensure that the fittest individuals are retained at the next generation.
Abstract: The elephant herding optimization (EHO) is a recent swarm intelligence algorithm. This algorithm simulates the clan updating and separation behavior of elephants. The EHO method has been successfully deployed in various fields. However, a more reliable implementation of the standard EHO algorithm still requires improving the control and selection of the parameters, convergence speed, and efficiency of the optimal solutions. To cope with these issues, this study presents an improved EHO algorithm terms as IMEHO. The proposed IMEHO method uses a global velocity strategy and a novel learning strategy to update the velocity and position of the individuals. Furthermore, a new separation method is presented to keep the diversity of the population. An elitism strategy is also adopted to ensure that the fittest individuals are retained at the next generation. The influence of the parameters and strategies on the IMEHO algorithm is fully studied. The proposed method is tested on 30 benchmark functions from IEEE CEC 2014. The obtained results are compared with other eight metaheuristic algorithms and evaluated according to Friedman rank test. The results imply the superiority of the IMEHO algorithm to the standard EHO and other existing metaheuristic algorithms.

Journal ArticleDOI
TL;DR: A comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions are provided.
Abstract: The Harmony Search Algorithm (HSA) is a swarm intelligence optimization algorithm which has been successfully applied to a broad range of clustering applications, including data clustering, text clustering, fuzzy clustering, image processing, and wireless sensor networks. We provide a comprehensive survey of the literature on HSA and its variants, analyze its strengths and weaknesses, and suggest future research directions.

Journal ArticleDOI
TL;DR: A comprehensive review of Dragonfly algorithm and its new variants classified into modified and hybrid versions and describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering.
Abstract: Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

Journal ArticleDOI
TL;DR: Chaos has been integrated into the standard BSA, for the first time, in order to enhance the global convergence feature by preventing premature convergence and stumbling in the local solutions.
Abstract: Swarm intelligence based optimization methods have been proposed by observing the movements of alive swarms such as bees, birds, cats, and fish in order to obtain a global solution in a reasonable time when mathematical models cannot be formed. However, many swarm intelligence algorithms suffer premature convergence and they may stumble in local optima. Bird swarm algorithm (BSA) is one of the most recent swarm-based methods that suffers the same problems in some situations. In order to obtain a faster convergence with high accuracy from the swarm based optimization algorithms, different methods have been utilized for balancing the exploitation and exploration. In this paper, chaos has been integrated into the standard BSA, for the first time, in order to enhance the global convergence feature by preventing premature convergence and stumbling in the local solutions. Furthermore, a new research area has been introduced for chaotic dynamics. The standard BSA and the chaotic BSAs proposed in this paper have been tested on unimodal and multimodal unconstrained benchmark functions, and on constrained real-life engineering design problems. Generally, the obtained results from the proposed novel chaotic BSAs with an appropriate chaotic map can outperform the standard BSA on benchmark functions and engineering design problems. The proposed chaotic BSAs are expected to be used effectively in many complex problems in future by integrating enhanced multi-dimensional chaotic maps, time-continuous chaotic systems, and hybrid multi-dimensional maps.

Journal ArticleDOI
TL;DR: The fuzzy brain-storm optimization algorithm for medical image segmentation and classification was proposed, a combination of fuzzy and brain-storms optimization techniques, and it seems promising and outperforms the other techniques with better results in this analysis.
Abstract: Brain tumor is the most severe nervous system disorder and causes significant damage to health and leads to death. Glioma was a primary intracranial tumor with the most elevated disease and death rate. One of the most widely used medical imaging techniques for brain tumors is magnetic resonance imaging (MRI), which has turned out the principle diagnosis system for the treatment and analysis of glioma. The brain tumor segmentation and classification process was a complicated task to perform. Several problems could be more effectively and efficiently solved by the swarm intelligence technique. In this paper, the fuzzy brain-storm optimization algorithm for medical image segmentation and classification was proposed, a combination of fuzzy and brain-storm optimization techniques. Brain-storm optimization concentrates on the cluster centers and provides them the highest priority; it might fall in local optima like any other swarm algorithm. The fuzzy perform several iterations to present an optimal network structure, and the brain-storm optimization seems promising and outperforms the other techniques with better results in this analysis. The BRATs 2018 dataset was used, and the proposed FBSO was efficient, robust and mainly reduced the segmentation duration of the optimization algorithm with the accuracy of 93.85%, precision of 94.77%, the sensitivity of 95.77%, and F1 score of 95.42%.

Journal ArticleDOI
TL;DR: The experimental results demonstrate that PSO-Xgboost model outperforms other comparative models in precision, recall, macro-average (macro) and mean average precision (mAP), especially when identifying minority groups of attacks like U2R and R2L.
Abstract: Network intrusion detection system (NIDS) is a commonly used tool to detect attacks and protect networks, while one of its general limitations is the false positive issue. On the basis of our comparative experiments and analysis for the characteristics of the particle swarm optimization (PSO) and Xgboost, this paper proposes the PSO-Xgboost model given its overall higher classification accuracy than other alternative models such like Xgboost, Random Forest, Bagging and Adaboost. Firstly, a classification model based on Xgboost is constructed, and then PSO is used to adaptively search for the optimal structure of Xgboost. The benchmark NSL-KDD dataset is used to evaluate the proposed model. Our experimental results demonstrate that PSO-Xgboost model outperforms other comparative models in precision, recall, macro-average (macro) and mean average precision (mAP), especially when identifying minority groups of attacks like U2R and R2L. This work also provides experimental arguments for the application of swarm intelligence in NIDS.

Journal ArticleDOI
TL;DR: This article presents a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective, and intends to provide novel insights into the latest technologies in UAV Swarm intelligence.
Abstract: The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy, and intelligence. In this article, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

Journal ArticleDOI
TL;DR: A Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA and demonstrates that YYFA provides highly competitive performance in terms of the tested algorithms.
Abstract: Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.

Journal ArticleDOI
TL;DR: Improved versions of the tree growth and firefly algorithms that improve the original implementations are proposed that establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.
Abstract: Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.

Proceedings ArticleDOI
15 Jun 2020
TL;DR: An improved version of the firefly algorithm has been applied to improve the network lifetime maximization and conducted simulations have proven that the proposed metaheuristic achieves better and more consistent performance than other algorithms.
Abstract: We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications including military, environment monitoring, surveillance and body area sensor networks. One of the biggest challenges each wireless sensor network has to handle is the network lifetime maximization. To achieve this, numerous clustering algorithms have been created, with the goal to improve energy consumption throughout the network by balancing the energy consumption overall nodes. All clustering algorithms incorporate load balancing to achieve energy efficiency. One of the basic and most important algorithms in use is LEACH. Swarm intelligence metaheuristics have already been applied in solving numerous problems of wireless sensor networks, including lifetime optimization, localization and many other NP hard problems with promising results, as can be seen in the literature overview. In the research proposed in this paper, an improved version of the firefly algorithm has been applied to improve the network lifetime. The firefly algorithm was used to help in forming the clusters and selection of the cluster head. Additionally, we have evaluated the performance of the improved firefly algorithm by comparing it to the LEACH, basic firefly algorithm and particle swarm optimization, that were all tested on the same network infrastructure model. Conducted simulations have proven that our proposed metaheuristic achieves better and more consistent performance than other algorithms.

Journal ArticleDOI
TL;DR: A binary version of the COA, named Binary COA (BCOA) applying to select the optimal feature subset for classification, based on the hyperbolic transfer function in a wrapper model is proposed.