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Summary: The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations. [ABSTRACT FROM AUTHOR]

Parkinson’s disease is caused by a disruption in the chemical products that enables the communication between brain cells. The brain’s dopamine cells are responsible for movement control, adaptability, and fluidity. Parkinson’s motor symptoms manifest when 60–80% of these cells are damaged due to insufficient dopamine. Researchers are working to find a way to identify the non-motor symptoms that manifest early detection in the disease to stop the disease’s progression because it is believed that the disease starts many years before the motor symptoms. This research presents Parkinson’s disease diagnosis based on deep learning. Processes for feature selection and classification encompass the suggested diagnosis technique. The proposed model searches for the best subset of characteristics using the Improved Glowworm Swarm Optimization (IGSO) algorithm. Radial Basis Functions Networks (RBFN) classifiers evaluate the chosen features. The suggested model is tested using datasets from Parkinson’s Handwriting samples and Parkinson’s Speech and voice with various sound recordings. With an accuracy of about 95.78%, the suggested algorithm forecasts Parkinson’s disease using the VoicePD dataset more precisely. [ABSTRACT FROM AUTHOR]

Glowworm Swarm Optimization (GSO) is a population-based optimization algorithm that successfully solves numerous optimization problems. Nonetheless, the convergence speed required to reach optimal solutions can be made more efficient by skipping local optima. Also, considerable attention to tuning the parameter of the algorithm is crucial to improve the convergence speed. In this study, three variants of GSO are proposed using various mutation operators (Gaussian, Cauchy, and Lévy) to improve its convergence speed and prevent it from getting stuck in a local optimum. The small and random changes provided by the Gaussian mutation help in fine-tuning the position of the Glowworms. Meanwhile, the Cauchy mutation offers large changes that can assist the movement operator of the GSO in exploring wide area of the search space. Also, Lévy mutation is characterized by occasional large jumps, which have the potential to explore the problem space effectively. The performance and accuracy of the proposed methods are studied based on famous multimodal and unimodal benchmark test functions, as well as the CEC2014 test suite. Additionally, we have experimented with the proposed algorithm on a set of engineering problems. The effects of the parameter settings on the improved GSO are discussed using Response Surface Methodology (RSM). Results revealed that the suggested GSO algorithms, offer better solutions than the basic GSO algorithm and other GSO variants. In comparison to the state-of-the-art algorithms, the proposed GGSO obtained the best results for 68.75%, and 63.33% of the benchmark test functions and CEC2014, respectively. Additionally, statistical tests show the superiority of GGSO over other modified algorithms.

People all over the world have experienced severe panic as a result of the novel coronavirus (COVID-19). To secure their future, it is crucial to undertake thorough evaluations in their psychological, physical, and social domains, comprehend the potential outcomes of patients recovering from it, and ascertain whether they have any other harmful diseases. This possible outcome for people recovered from Covid 19 was predicted by collecting data from people who had previously been infected with this virus to determine the effects they had, using intelligent techniques. The GSO algorithm was used for feature selection, and for hyper-parameter tuning for the Random Forest (RF) algorithm used in the prediction process in order to make predictions to identify the effects that may accrue on recoveries persons. This model was evaluated using different metrics after performing multiple processing operations on the data and using the GSO algorithm to perform the feature selection process in order to obtain the important features. Good results were obtained for each expected effect, as the highest AUC was obtained when predicting the impact of the gastrointestinal tract of recovered persons, which is 0.91. This will then reveal the effects that Covid-19 has had on people after they have recovered. This will assist in anticipating possible results to provide counseling and psychological support, as well as some recommended guidelines for healing patients and the community to return to a normal life.

In recent years, flying ad hoc networks (FANETs) have witnessed a notable increase in its applications after the onset of the collaborations of the small unmanned aerial vehicles (UAVs). Because of its inherent characteristics, FANETs are used in diverse application ranging from the military to civil domain. Conversely, there are certain issues pertaining to the communication among the UAVs in view of the high mobility and limited battery resources available in the UAVs, resulting in their short lifetime. The paper is an attempt to address these issues plaguing to the short lifespan of the UAVs. In this paper, we propose a hybrid bio-inspired algorithm HGSOFA for optimizing cluster head (CH) selection in a FANETs. HGSOFA utilizes the hybrid implementation of glowworm swarm optimization (GSO) and firefly algorithm (FA). In this paper, we explain the step-by-step working of the HGSOFA and then performance is evaluated through rigorous simulations. Two separate network areas with varying node density is considered for conducting all the simulations. A robust experimental environment is developed using Taguchi and orthogonal methods. HGSOFA's performance is tested against the conventional GSO and FA algorithms in respect of cluster building time, energy consumption and first node death. Comparable results have showcased the advantages of the HGSOFA as compared to other algorithms. [ABSTRACT FROM AUTHOR]

Image retrieval plays an important role in the analysis of obtained decisive visual information. The presence of visual inconsistency in visual appearance decreases the retrieval accuracy and many of the present retrieval methods emphasize single-source retrieval with the assumption of queries and databases distributions being similar. The number of features obtained with the traditional approach in which some of them are redundant, correlated, and sometimes noisy, increases the model feature space complexity and decreases interpretability. From the study of previous work, it is evident that feature fusion with cross-domain retrieval has not been addressed thoroughly so far. Thus, to deal with these issues, this extracts the optimal combination of the multi-modal features and fuses for enhancing retrieval accuracy. The complementary features obtained are effective with the traditional approach for the improvement of representation and retrieval effectiveness. Thus, Image Retrieval using Single and Cross-Domain Feature Fusion (SCDFF) is proposed in this work. The multi-modal features are extracted with Texture, Color, Statistical, and Scale Invariant Feature Transform (SIFT) descriptors to perform the retrieval process. The feature vector is fused using an optimized weight value which is obtained from Glowworm Swarm Optimization (GSO) algorithm and the image similarity is computed with K-Nearest Neighbor. An empirical analysis is performed to evaluate the proposed model and from the results obtained, it is evident that this work outperforms existing approaches in terms of accuracy. The novelty of this work lies in the fact of Single-Domain Feature Fusion (SDFF) and Cross-Domain Feature Fusion (CDFF) with optimization for Image Retrieval. [ABSTRACT FROM AUTHOR]

Recently, the exponential utilization of Internet has posed several cybersecurity issues in social networks. Particularly, cyberbulling becomes a common threat to users in real time environment. Automated detection and classification of cyberbullying in social networks become an essential task, which can be derived by the use of machine learning (ML) and deep learning (DL) approaches. Since the hyperparameters of the DL model are important for optimal outcomes, appropriate tuning strategy becomes important by the use of metaheuristic optimization algorithms. In this study, an effective glowworm swarm optimization (GSO) with deep neural network (DNN) model named EGSO-DNN is derived for cybersecurity in social networks. The proposed EGSO-DNN technique is mainly intended to identify the presence of cyberbullying on social networking sites. Besides, the EGSO-DNN technique involves different levels of pre-processing to transform the raw data into useful format. In addition, word2vec based feature extraction technique is applied to generate a set of feature vectors. Finally, the DNN model is used for the detection and classification of the DNN model where the hyperparameters of the DNN model are adjusted proficiently by the use of GSO algorithm. In order to ensure the supremacy of the EGSO-DNN technique, a series of simulations were carried out and the results are tested using benchmark datasets. The comparative analysis reported the improvements of the EGSO-DNN technique over the recent approaches maximum prec_n, reca_l, and F1_score of 0.9974, 0.9959, and 0.9966. [ABSTRACT FROM AUTHOR]

In a cloud data center, one of the most important problems is using novel virtual machine placement strategy to promote the physical resource utilization.An approach of glowworm swarm optimization based virtual machine placement for cloud data centers called GSO-VMP was proposed.In the virtual placement, GSO algorithm was used to find a near-optimal solution.Each physical host had a luciferin value which represented the available CPU utilization.Whenever a VM was placed to a physical host, luciferin value of this physical host was updated.GSO-VMP algorithm could search the more available physical host within local range and thus the virtual migration numbers had been decreased and low energy consumption had been obtained.GSO-VMP had been evaluated using CloudSim with real-world workload data.The experimental results show that GSO-VMP has good performance in resource wastage and energy consumption.

The Wireless Sensor Networks (WSN) is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission. The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head. The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network. The proposed model is a hybridization of Glowworm Swarm Optimization (GSO) and Artificial Bee Colony (ABC) algorithm for the better identification of cluster head. The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35% better value and reduction of time complexity upto 1.46%. Above all, the proposed model is 16% ahead of alive node count when compared with the existing methodologies. [ABSTRACT FROM AUTHOR]

Summary: In the wireless sensor networks energy consumption is broadest and widely explored area of research. The solution for energy optimization encompasses various techniques such as efficient routing protocols, data scheduling, clustering, hardware redesigning, supervised and unsupervised network learning algorithm, and so forth. Compared with all the methods that has been so far discussed, swarm intelligence (SI) is considered to be the optimal way to find solution for reducing energy consumption as it is simple and the network formation is understood by the natural mechanism present in nature. SI approaches include ant colony optimization (ACO), particle swarm optimization, glowworm swarm optimization (GSO), and so forth. In this article, the authors provide the solution for the energy conservation problem through efficient GSO methods combined ACO. The revised beaconing glowworm swarm optimization ant colony optimization algorithm will be applied on the sensor network divided into swarms based on glowworms, the ants are introduced in the network that would parse the network by visiting the swarm heads with the principle of ACO behind it. The algorithm is tested on MATLAB 2015a for performance comparison with the HM‐ACOPSO method with depicts energy conservation and efficiency in data collection. [ABSTRACT FROM AUTHOR]

Nowadays, a great attention is focusing on the study of the emotional content in speech signals since the speech signal is one of the quickest and natural tactic to communicate among humans, and thus, many systems have been suggested to recognize the emotional content of a spoken utterance. This paper schemes two contributions: Gender recognition and Emotion recognition. In the first contribution, there has two phases: feature extraction and classification. The pitch feature is extracted and given for classification using k-Nearest Neighboring classifier. In the second contribution, features like Non-Negative Matrix Factorization and pitch are extracted and given as the input to Adaptive Fuzzy classifier to recognize the respective emotions. In addition, the limits of membership functions are optimally chosen using a renowned optimization algorithm namely glowworm swarm optimization (GSO). Thus the proposed adaptive Fuzzy classifier using GSO is termed as GSO-FC. The performance of proposed model is compared to other conventional algorithms like Grey Wolf Optimization, FireFly, Particle Swarm Optimization, Artificial Bee Colony and Genetic Algorithm in correspondence with varied performance measures like Accuracy, Sensitivity, Specificity, Precision, False positive rate, False negative rate, Negative Predictive Value, False Discovery Rate, F1 Score and Mathews correlation coefficient. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) comprise a collection of independent, compact-sized, and inexpensive sensor nodes, which are commonly used to sense the physical parameters in geographical locations and transmit them to base stations (BSs). Since clustering and routing are considered commonly used energy efficient techniques, several metaheuristic algorithms have been employed to determine optimal cluster heads (CHs) and routes to destinations. However, most metaheuristic techniques have failed to achieve effective clustering and routing solutions in a large search space, and the probability of generating optimal solutions is also considerably reduced. To resolve these issues, this paper presents a new metaheuristic quantum worm swarm optimization-based clustering with a secure routing protocol for MANETs, named QGSOC-SRP. The presented QGSOC-SRP technique follows a two-stage process, namely optimal CH selection and route selection. First, the QGSO algorithm derives a fitness function using four variables, the energy, distance, node degree, and trust factor, for the optimal selection of secure CHs. Second, the SRP using the oppositional gravitational search algorithm (OGSA) is applied for the optimal selection of routes to the BS. The traditional GSA is inspired by the law of gravity and interaction among masses. To improve the effectiveness of the GSA, the OGSA is derived based on the opposition-based learning concept for population initialization and generation jumping. To validate the results regarding the effectiveness of the presented OGSOC-SRP technique, a set of experiments was performed, and the results were determined using distinct measures such as the energy consumption, network lifetime, throughput, and end-to-end delay. [ABSTRACT FROM AUTHOR]

In a cloud data center, one of the most important problems is using novel virtual machine placement strategy to promote the physical resource utilization. An approach of glowworm swarm optimization based virtual machine placement for cloud data centers called GSO-VMP was proposed. In the virtual placement, GSO algorithm was used to find a near-optimal solution. Each physical host had a luciferin value which represented the available CPU utilization. Whenever a VM was placed to a physical host, luciferin value of this physical host was updated. GSO-VMP algorithm could search the more available physical host within local range and thus the virtual migration numbers had been decreased and low energy consumption had been obtained. GSO-VMP had been evaluated using CloudSim with real-world workload data. The experimental results show that GSO-VMP has good performance in resource wastage and energy consumption. [ABSTRACT FROM AUTHOR]

This study compared swarm-based algorithms in terms of their effectiveness in improving the design of facilities in container terminals (CTs). The design was conducted within the framework of stochastic discrete optimization and involved determining the number of equipment needed in CTs by considering variations in demand and the productivity of facilities—issues that are rarely elaborated in CT design. Variations were identified via Monte Carlo simulation characterized by a particular distribution. The conflicting issue due to increments in equipment investment that possibly cause the distribution delays was also modeled, specifically in relation to the increasing number of trucks used in terminals. Given that the optimization problem is typified by numerous combinations of actions, the swarm-based algorithms were deployed to develop a feasible solution. A new variant of glowworm swarm optimization (GSO) was then proposed and compared with particle swarm optimization (PSO) algorithms. The numerical results showed that the performance of the proposed GSO is superior to that of PSO algorithms.

In order to solve the problem that temperature drift of silicon piezoresistive pressure sensor affects the accuracy of engineering measurement,proposed a temperature compensation strategy for BP neural network based on glowworm swarm optimization. The generalized BP neural network is used to optimize the weights and thresholds by using the firefly algorithm,thus improving the generalization performance and searching speed of the neural network,carried out temperature compensation of pressure sensor by optimized BP neural network. The temperature compensation performance of optimized BP neural network compares to that of conventional neural network and particle swarm optimization neural network. The results showed that compared with the conventional neural network and PSO optimization BP neural network,the optimized GSO optimization BP neural network is effective.The compensation error of GSO-BP neural network is 52% less than that of BP and 23% less than that of PSO-BP.Considering the time of compensation,the comprehensive performance of the BP neural network optimized by GSO is better.The compensated sensor data meet the experimental requirements of the subject.The compensation algorithm is feasible.

Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson's disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE–GSO–ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE–GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE–GSO–ANFIS over the similar methods in predicting medical disorders. [ABSTRACT FROM AUTHOR]

Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects. In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort. The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models. Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2. The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones. many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) are a particular type of Mobile ad hoc networks (MANET) in which the vehicles are considered as nodes. Due to rapid topology changing and frequent disconnection makes it difficult to design an efficient routing protocol for routing data among vehicles. In this paper, a new routing protocol based on glowworm swarm optimization algorithm is provided. Using the glowworm algorithm the proposed protocol detects the optimal route between three-way and intersections. Then, the packets are delivered based on the selected routes. The proposed algorithm by using the glowworm swarm optimization algorithm, which is a distributed heuristic algorithm, assigns a value to each route from a source to the destination. Then a route with the higher value is selected to send messages from the source to the destination. The simulation results show that the proposed algorithm has a better performance than the similar algorithms.

Glowworm swarm optimization (GSO) and bacterial foraging optimization algorithm (BFOA) are two popular swarm intelligence optimization algorithms (SIOAs). However, both GSO and BFOA show some difficulties when solving many-objective optimization problems (MaOPs). To challenge MaOPs, a coupling approach based on GSO and BFOA is proposed in this paper. To implement the coupling method, an external archive is established to save the best solutions found so far. The internal populations in GSO and BFOA can exchange the search information with the external archive in the evolutionary process. Simulation experiments are verified on two benchmark sets (DTLZ and WFG) with 3 to 15 objectives. The performance of our approach is compared with five other famous algorithms including NSGA-III, KnEA, MOEA/D-DE, GrEA and HypE. Results prove the effectiveness of our approach.

Handling large data in geographically distributed information centers with resource and cost optimization is a key challenge. With several approaches being designed, handling a large volume of data in multiple datacenters in an inappropriate manner yet is considered to be a time-consuming process. To address these issues, a Multivariate Metaphor based Metaheuristic Glowworm Swarm Map-Reduce Optimization (MM-MGSMO) technique is presented. Here, with search space and large data volume as input for geo-distributed datacenters, glowworm (i.e. virtual machine) population is initialized. With each glowworm possessing a certain amount of luciferin (i.e. objective function), multiple objective functions (i.e. bandwidth, storage capacity, energy and computation cost) are defined for each virtual machine. Next, the glowworm position is updated according to the neighboring factor by means of probability. Followed by this, MapReduce function identifies the optimal virtual machine and accordingly allocation is performed, therefore improving data allocation efficiency. Besides, the workload is assigned across datacenters, reduction in computation cost and storage capacity is guaranteed. Experimental evaluation of MM-MGSMO approach with existing methods attained improved performances with factors such as data allocation efficiency, false-positive rate, storage capacity compared with other cutting edge technologies such as Joint optimization algorithm and Game theory-based dynamic resource allocation strategy. [ABSTRACT FROM AUTHOR]

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions. [ABSTRACT FROM AUTHOR]

Ensemble pruning has been widely used for enhancing classification ability employing a smaller number of classifiers. Ensemble pruning extracts a part of classifiers with good overall performance to form the final ensemble. Diversity and accuracy of classifiers are of vital importance for a successful ensemble. It is hard for the members in one ensemble to achieve both good diversity and high accuracy, simultaneously, because there is a tradeoff between them. Existing works usually search for the tradeoff in terms of diversity measures, or find it utilizing heuristic algorithms, which cannot gain the exact solution without exhaustive search. To address the above issue, a novel ensemble pruning method based on information exchange glowworm swarm optimization and complementarity measure, abbreviated EPIECM, is proposed using the combination of information exchange glowworm swarm optimization (IEGSO) and complementarity measure (COM). Firstly, multiple generated classifiers are utilized to construct a pool of learners who perform diversely. Secondly, COM is employed to pre-prune the classifiers with poor comprehensive performance, and the pre-pruned ensemble is formed utilizing the retaining classifiers. Finally, the optimal subset of classifiers is combined from the remaining constituents after pre-pruning with IEGSO. Empirical results on 27 UCI datasets indicate that EPIECM significantly outperforms other techniques. [ABSTRACT FROM AUTHOR]

Ensemble pruning aims at attaining an ensemble composed of less size of leaners for improving classification ability. Extreme Learning Machine (ELM) is employed as a base learner in this work, in light of its salient features, an initial pool is constructed using ELM. An ensemble composed of ELMs with better performance and diversity can make it perform the best, but the average accuracy of the whole ELMs must be decreased as the increase of diversity among them. Hence there exists a balance between the diversity and the precision of ELMs. Existing works find it via diversity measures or heuristic algorithms, which cannot find the exact tradeoff. To solve the issue, ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization (EPEMBM) is proposed utilizing the integration of the proposed migratory binary glowworm swarm optimization (MBGSO) and margin distance minimization (MDM). First, the created ELMs in a pool can be pre-pruned by MDM, and it can markedly downsize the ELMs in the pool, and significantly alleviates its computation overhead. Second, the retaining ELMs are further pruned utilizing MBGSO, and the final ensemble is attained with a high efficiency. Experimental results on 21 UCI classification tasks indicate that EPEMBM outperforms techniques, and that its effectiveness and efficiency. It is a very useful tool for solving the selection problem of ELMs. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures. [ABSTRACT FROM AUTHOR]

Cloud computing is a very popular computing model, which grants a manageable infrastructure for various kinds of functions, like storage of data, application realization and presenting, and delivery of information. The concept is therefore very dynamically advancing in all kinds of organisations, including, in particular, the health care sector. However, effective analysis and extraction of information is a challenging issue that must find adequate solutions as soon as possible, since the medical scenarios are heavily dependent on such computing aspects as data security, computing standards and compliance, governance, and so on. In order to contribute to the resolution of the issues, associated with these aspects, this paper proposes a privacy-preserving algorithm for both data sanitization and restoration processes. Even though a high number of researchers contributed to the enhancement of the restoration process, the joint sanitization and restoration process still faces some problems, such as high cost. To attain better results with a possibly low cost, this paper proposes a hybrid algorithm, referred to as GlowWorm Swarm Employed Bee (GWOSEB) for realization of both data sanitization and data restoration process. The proposed GWOSEB algorithm is compared as to its performance with some of the existing approaches, such as the conventional Glowworm Swarm Optimization (GSO), FireFly (FF), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Genetically Modified Glowworm Swarm (GMGW), in terms of analysis involving the best, worst, mean, median and standard deviation values, sanitization and restoration effectiveness, convergence analysis, and sensitivity analysis of the generated optimal key. The comparison shows the supremacy of the developed approach. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are generically self-configuring and organizing networks with constrained communicational ability and energy supply. One of the crucial crises in WSN is the employment of energy effectual routing and load balancing protocol to improve network lifetime. Therefore, this work anticipated an effectual load balancing and routing strategies using the Glowworm swarm optimization approach (LBR-GSO). This LBR-GSO employs a pseudo-random route discovery algorithm and an enhanced pheromone trail-based updating strategy to handle the energy consumption of sensor nodes. It utilizes an effectual heuristic updating algorithm based on cost effectual energy measure to optimize route establishment. At last, to eliminate energy consumption that causes due to control overhead, LBR-GSO cast-off energy-based broadcasting strategy has been proposed. Here, WSNs environment is simulated in MATLAB for various application scenarios to compute LBR-GSO along with metrics such as energy efficiency, energy consumption and prolonging network lifetime. Outcomes derived from this comprehensive analysis determine that LBR-GSO offers an effectual enhancement in contrary to prevailing approaches like ACO, EE-ACO and s-Ant approaches. [ABSTRACT FROM AUTHOR]

This study compared swarm-based algorithms in terms of their effectiveness in improving the design of facilities in container terminals (CTs). The design was conducted within the framework of stochastic discrete optimization and involved determining the number of equipment needed in CTs by considering variations in demand and the productivity of facilities-issues that are rarely elaborated in CT design. Variations were identified via Monte Carlo simulation characterized by a particular distribution. The conflicting issue due to increments in equipment investment that possibly cause the distribution delays was also modeled, specifically in relation to the increasing number of trucks used in terminals. Given that the optimization problem is typified by numerous combinations of actions, the swarm-based algorithms were deployed to develop a feasible solution. A new variant of glowworm swarm optimization (GSO) was then proposed and compared with particle swarm optimization (PSO) algorithms. The numerical results showed that the performance of the proposed GSO is superior to that of PSO algorithms. [ABSTRACT FROM AUTHOR]

When the basic glowworm swarm optimization (GSO) algorithm optimizes the multi-peak function, the solution accuracy is not high, the later convergence is slow. To solve these problems, the fluorescent factor is introduced to adaptively adjust the step length of the firefly, an adaptive step length firefly optimization algorithm is proposed, this algorithm is an improved self-adaptive step glowworm swarm optimization (ASGSO). In this algorithm, the behavior of glowworms are developed, the step size is dynamically adjusted by the fluorescent factor, the algorithm avoids falling into a local optimum and improves the optimization speed and accuracy. The simulation results show that the improved ASGSO can search for global optimization more quickly and precisely. [ABSTRACT FROM AUTHOR]

In order to solve the problem of high access overhead to replicas, this paper proposed a replica placement algorithm based on discrete glowworm swarm optimization. Through mathematical mode establishment for user access overhead, this algorithm computed the fitness function of glowworm position, updated individual position and then obtained appropriate nodes for replica placement. It conducted several simulations and compared with the replica placement strategy based on ant algorithm.The results show that the proposed algorithm can select appropriate nodes for replica placement, and has a better convergence and reduces the replica access overhead. [ABSTRACT FROM AUTHOR]

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

Human activity recognition (HAR) technology that analyzes and fuses the data acquired from various homogeneous or heterogeneous sensor sources has motivated the development of enormous human-centered applications such as healthcare, fitness, ambient assisted living and rehabilitation. The concurrent use of multiple sensor sources for HAR is a good choice because the plethora of user information provided by the various sensor sources may be useful. However, a multi-sensor system with too many sensors will bring large power consumption and some sensor sources may bring little improvements to the performance. Therefore, the multi-sensor deployment research that can gain a tradeoff among computational complexity and performance is imperative. In this paper, we propose a multi-sensor-based HAR system whose sensor deployment can be optimized by selective ensemble approaches. With respect to optimization of the sensor deployment, an improved binary glowworm swarm optimization (IBGSO) algorithm is proposed and the sensor sources that have a significant effect on the performance of HAR are selected. Furthermore, the ensemble learning system based on optimized sensor deployment is constructed for HAR. Experimental results on two datasets show that the proposed IBGSO-based multi-sensor deployment approach can select a smaller number of sensor sources while achieving better performance than the ensemble of all sensors and other optimization-based selective ensemble approaches.

The probabilistic Delay Tolerant Network (DTN) routing has been adjusted for vehicular network (VANET) routing through numerous works exploiting the historic routing profile of nodes to forward bundles through better Store-Carry-and-Forward (SCF) relay nodes. In this paper, we propose a new hybrid swarm-inspired probabilistic Vehicular DTN (VDTN) router to optimize the next-SCF vehicle selection using the combination of two bio-metaheuristic techniques called the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). The FA-based strategy exploits the stochastic intelligence of fireflies in moving toward better individuals, while the GSO-based strategy mimics the movement of glowworm towards better area for displacing and food foraging. Both FA and GSO are executed simultaneously on each node to track better SCF vehicles towards each bundle’s destination. A geography-based recovery method is performed in case no better SCF vehicles are found using the hybrid FA–GSO approach. The proposed FA–GSO VDTN scheme is compared to ProPHET and GeoSpray routers. The simulation results indicated optimized bundles flooding levels and higher profitability of combined delivery delay and delivery probability.

According to the characteristics of glowworm swarm optimization（GSO）algorithm, to overcome the disadvantages of premature convergence, a new method called “baffle effect”was proposed. The diversity of swarm was expanded. Based on the analysis of the model of nonlinear blind source separation（NBSS）, using traversing glowworm swarm twice, comparing evaluation function, the best result was get. Their validity is confirmed by effect of the signal separation test.

Robot path planning is a task to determine the most viable path between a source and destination while preventing collisions in the underlying environment. This task has always been characterized as a high dimensional optimization problem and is considered NP-Hard. There have been several algorithms proposed which give solutions to path planning problem in deterministic and non-deterministic ways. The problem, however, is open to new algorithms that have potential to obtain better quality solutions with less time complexity. The paper presents a new approach to solving the 3-dimensional path planning problem for a flying vehicle whose task is to generate a viable trajectory for a source point to the destination point keeping a safe distance from the obstacles present in the way. A new algorithm based on discrete glowworm swarm optimization algorithm is applied to the problem. The modified algorithm is then compared with Dijkstra and meta-heuristic algorithms like PSO, IBA and BBO algorithm and their performance is compared to the path optimization problem. [ABSTRACT FROM AUTHOR]

In recent years many heuristic algorithms have been proposed to solve task scheduling problems in the cloud environment owing to their optimization capability. This article proposes a hybrid glowworm swarm optimization (HGSO) based on glowworm swarm optimization (GSO), which uses a technique of evolutionary computation, a strategy of quantum behaviour based on the principle of neighbourhood, offspring production and random walk, to achieve more efficient scheduling with reasonable scheduling costs. The proposed HGSO reduces the redundant computation and the dependence on the initialization of GSO, accelerates the convergence and more easily escapes from local optima. The conducted experiments and statistical analysis showed that in most cases the proposed HGSO algorithm outperformed previous heuristic algorithms to deal with independent tasks. [ABSTRACT FROM AUTHOR]