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With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm. [ABSTRACT FROM AUTHOR]

Multi‐objective optimization problems frequently appear in many diverse research areas and application domains. Metaheuristics, as efficient techniques to solve them, need to be easily accessible to users with different expertise and programming skills. In this context, metaheuristic optimization frameworks are helpful, as they provide popular algorithms, customizable components and additional facilities to conduct experiments. Due to the broad range of available tools, this paper presents a systematic evaluation and experimental comparison of 10 frameworks, covering from multi‐purpose, consolidated tools to recent libraries specifically designed for multi‐objective optimization. The evaluation is organized around seven characteristics: search components and techniques, configuration, execution, utilities, external support and community, software implementation and performance. An analysis of code metrics and a series of experiments serves to assess the last two features. Lesson learned and open issues are also discussed as part of the comparative study. The outcomes of the evaluation process reveal a contrasted support to recent advances in multi‐objective optimization, with a lack of novel algorithms and variety of metaheuristics other than evolutionary algorithms. The experimental comparison also reports significant differences in terms of both execution time and memory usage under demanding configurations. [ABSTRACT FROM AUTHOR]

Massively complex systems, such as social networks (Camacho, Panizo-LLedot, Bello-Orgaz, Gonzalez-Pardo, & Cambria, 2020; Lara-Cabrera et al., 2017), renewable energy problems (Twidell & Weir, 2015), or Internet-of-Things problems (Lin et al., 2017), generate massive amounts of data. This special issue welcomed submissions of original papers introducing research results on all the aspects covering the application of CI algorithms to massively complex systems, ranging from concepts and theoretical developments to advanced technologies and innovative applications. GRASP algorithm is also used in the paper by Pérez-Peló, Sánchez-Oro, & Duarte (2020), where the authors tackle the I i -separator problem. [Extracted from the article]

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment. [ABSTRACT FROM AUTHOR]

Summary: Industrial internet‐of‐things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high‐order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high‐order optimal density selection (SHODS3O‐CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization‐based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high‐order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods. [ABSTRACT FROM AUTHOR]

Most often clinicians require automated computer‐aided MRI classification techniques to substantiate the status of dementia accurately. In this research paper, dragonfly‐based features are used to improve the accuracy of well‐known swarm intelligence algorithms specifically particle swarm optimization, artificial bee colony, and ant colony optimization in dementia classification. Cross‐sectional MRI of 65 non‐dementia and 52 dementia subjects were collected from the OASIS database and analyzed. The dementia classification performance of above‐mentioned three individual swarm intelligence algorithms is compared with non‐swarm intelligence algorithm—Fuzzy C means. A further comparison was made on the improvisation of above‐mentioned swarm intelligence algorithms while using dragonfly‐based features and Fuzzy C means‐based features. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly‐based features for improving the performance of individual swarm intelligence algorithms in dementia classification. With proper weight parameters, Dragonfly‐particle swarm optimization hybrid classifier yields the highest accuracy of 87.18%, whereas all the above‐mentioned individual classifiers yield accuracy of less than 66%. [ABSTRACT FROM AUTHOR]

Motivated by biological swarms occurring in nature, there is recent interest in developing swarms comprised completely of engineered agents. The main challenge for developing swarm guidance laws compared to earlier formation flying and multi-vehicle coordination approaches is the sheer number of agents involved. While formation flying applications might involve up to 10 to 20 agents, swarms are desired to contain hundreds to many thousands of agents. In order to deal with the sheer size, the present paper makes a break with past deterministic methods, and considers the swarm as a statistical ensemble for which guidance can be performed from a probabilistic point of view. The probability-based approach takes advantage of the law of large numbers, and leads to computationally tractable and implementable swarm guidance laws. Agents following a probabilistic guidance algorithm make statistically independent probabilistic decisions based solely on their own state, which ultimately guides the swarm to the desired density distribution in the configuration space. Two different synthesis methods are introduced for designing probabilistic guidance laws. The first is based on the Metropolis-Hastings (M-H) algorithm, and the second is based on using linear matrix inequalities (LMIs). The M-H approach ensures convergent swarm behavior subject to enforced desired motion constraints, while the LMI approach additionally ensures exponential convergence with a prescribed decay rate, and allows minimization of a cost function that reflects fuel expenditure. In addition, both algorithms endow the swarm with the property of self-repair, and the capability to strictly enforce zero-probability keep-out regions. This last property requires a slight generalization of the Perron-Frobenius theory, and can be very useful in swarm applications that contain regions where no agents are allowed to go. Simulation examples are given to illustrate the methods and demonstrate desired properties of the guided swarm. [ABSTRACT FROM AUTHOR]

Ant‐based clustering algorithms are inspired by the behaviour of real ants. In most ant‐based clustering algorithms, each ant lacks global visibility and only uses local search to find the best place on a grid to drop its data item. This paper presents a new clustering method called learning automata‐based clustering algorithm using learning automata (LA) and ant swarm intelligence. In this paper, the problem of finding the best location of grid for dropping a data item is solved using LA. We introduced a new drop operation using the global search capability of LA that can increase the quality of clustering. In the proposed method, each ant is equipped with an LA and a two‐dimensional grid is partitioned into a number of clusters. To drop a data item, the LA of each ant will be responsible for finding one of the best clusters on a grid unlike other methods that use the random search of a grid. To evaluate the results, some clustering evaluation criteria, such as the number of obtained clusters, f‐measure, Rand index, intracluster variance, Dunn index, error rate, and time cost were employed. Additionally, the proposed algorithms were compared with k‐means, Lumer and Faieta, ant clustering algorithm, modified version of the ant‐clustering algorithm, Chaotic Ant clustering (CAC), chaotic ant clustering algorithm, adaptive ant‐based clustering algorithm, adaptive artificial ant clustering algorithm, and ant clustering algorithm with information theoretic learning algorithms on both real and synthetic datasets. Experimental results show that the proposed method can increase the efficiency of the real dataset by 11.13% and that of the synthetic dataset by 16.11%. It also reduces the number of neighbouring function calls by 16% for the real dataset and by 28% for the synthetic dataset. [ABSTRACT FROM AUTHOR]

Active queue management (AQM) is an effective solution for the congestion control problem. It can achieve high quality of service (QoS) by reducing the packet dropping probability and network utilization. Three robust control algorithms are proposed in this paper in order to design robust AQM schemes: conventional [ABSTRACT FROM AUTHOR]

This study presents a cohesive configuration controller for distributed space coverage by a swarm of robots. The goal is to build a dense, convex network that is robust against disconnection while robots are flocking with only incomplete knowledge about the network. The controller is an integrated framework of two different algorithms. First, we present a boundary force algorithm: physics‐based swarm intelligence that borrows the concept of surface tension force between liquid molecules. The combination of such a force with conventional flocking produces a convex and dense configuration without knowledge of the complete geometry of a robot network. Second, robots distributively determine when a configuration is on the verge of disconnection by identifying a local articulation point—a region where the removal of a single robot will change the local topology. When such a point is detected, robots switch their behavior to clustering, which aggregates them around the vulnerable region to remove every articulation point and retain a connected configuration. Finally, we introduced an index that objectively represents the level of risk of a robot configuration against the massive fragmentation, called vulnerability index. We provide theoretical performance analyses of each algorithm and validate the results with simulations and experiments using a set of low‐cost robots. [ABSTRACT FROM AUTHOR]

In this article, the paradigm of machine culture as an extension to machine intelligence is introduced. This new concept is modelled based on animal intelligence and culture. The example of orca intelligence and culture is considered as orcas possess in addition to skills allowing them to reach preys, the ability to transmit their culture from generation to generation. The orca intelligence is studied and then simulated to design an algorithm called Orca Algorithm (OA). OA consists in modelling the orca lifestyle and in particular the orcas social organization, echolocation behaviour and hunting techniques. In order to integrate the cultural dimension, OA was hybridized with the Cultural Algorithm (CA) to get an algorithm called Orca Cultural Algorithm (OCA). OCA was tested on 22 benchmark problems of the literature to evaluate its performance. Extensive experiments were first performed to set the algorithm parameters prior to measure its effectiveness and efficiency. In a second stage, OCA was adapted to discrete problems and applied to the maze game with four level of complexity. Additional experiments were held to compare the designed algorithm with recent state‐of‐the‐art evolutionary algorithms. The overall obtained results are very promising. [ABSTRACT FROM AUTHOR]

Nowadays, Long‐Term Evolution Advanced (LTE‐A) network is the primary innovation in 4G networks. The LTE‐A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE‐A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0‐1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0‐1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)–based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE‐A network than state‐of‐art methods in the proposed strategy. [ABSTRACT FROM AUTHOR]

A number of different gene selection approaches based on gene expression profiles (GEP) have been developed for tumour classification. A gene selection approach selects the most informative genes from the whole gene space, which is an important process for tumour classification using GEP. This study presents an improved swarm intelligent optimisation algorithm to select genes for maintaining the diversity of the population. The most essential characteristic of the proposed approach is that it can automatically determine the number of the selected genes. On the basis of the gene selection, the authors construct a variety of the tumour classifiers, including the ensemble classifiers. Four gene datasets are used to evaluate the performance of the proposed approach. The experimental results confirm that the proposed classifiers for tumour classification are indeed effective. [ABSTRACT FROM AUTHOR]

Summary: In edge computing, how to save energy among sustainable edge nodes is a hot topic. ON/OFF switching of edge nodes as a key point is efficient but still suffers from the long round‐trip time problem because of its centralized control manner. Especially in the wireless network, service coverage is proved to be NP‐Complete. To this end, we propose a Distributed Swarm intelligence‐based Energy‐saving algorithm (DSE). In DSE, pheromone and residual energy are used to calculate the wake‐up probability. Through the wake‐up probability, the edge node can be activated periodically and efficiently. In order to balance the energy in the whole system that contains massive edge nodes, we further use a correction factor, that is, DSE+, to adjust the wake‐up probability of the nodes. The proposed methods allow for distributed implementation without requiring a centralized control by the coordinator, and the pheromone accumulated temporally and spatially. In addition, they do not require node localization. Experiments show that both DSE and DSE+ can work as expected, and DSE+ with the correction factor improves the lifetime of the whole system at least 12.6% compared with the DSE without the correction factor. [ABSTRACT FROM AUTHOR]

Summary: Cognitive radio network (CRN) has occupied rich awareness on amazing potentiality of spectrum utilization for the secondary users. In CRN, effective spectrum management has received more attention by increasing number of communication devices and several techniques have been evolved in recent years. However, efficient spectrum sensing with minimum delay reducing number of spectrum handoffs and maximum throughputs are some of the major challenges to be addressed. In this proposed work, discrete Hughes‐Hartogs optimal and dual vector spectrum management (DHHO‐DVSM) in CRN are used for effective utilization of channel selection. The expected maximization Q channel selection is designed to obtain an efficient idle channels for improved transmission of the secondary user. The optimal channel is attained by the number of idle channels maintained from the Q channel selection list. Based on each SU requests, discrete Hughes‐Hartogs particle swarm optimal channel selection is proposed to find the best channel by computing the fitness function for discrete factor. Finally, dual constraints‐based vector spectrum handoff model is proposed to produce the hyper plane based on the probability conditional factors with the objective of minimizing the number of handoffs for secondary users. The simulation result shows that the proposed method has transparent edges over conventional channel selection models. In addition, results are verified to learn the optimal channel selection with minimal sensing delay, increasing throughput and minimal handoffs. The proposed algorithm DHHO‐DVSM is 22.8% comparatively better than E‐CRNs and 41% improvement than SpecPSO. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is one of the major causes of mortality worldwide. Knowledge about risk factors that increase the probability of developing CAD can help to understand the disease better and assist in its treatment. Recently, modern computer‐aided approaches have been used for the prediction and diagnosis of diseases. Swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modelled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. An approach for discovering classification rules of CAD is proposed. The work is based on the real‐world CAD data set and aims at the detection of this disease by producing the accurate and effective rules. The proposed algorithm is a hybrid binary‐real PSO, which includes the combination of categorical and numerical encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles, which take random values in the range of each attribute in the rule. Two different feature selection methods based on multi‐objective evolutionary search and PSO were applied on the data set, and the most relevant features were selected by the algorithms. The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD. [ABSTRACT FROM AUTHOR]

Summary: Elastic optical networks emerge as a reliable backbone platform covering the next‐generation connectivity requirements. It consists of advanced enabling components that provide the ability for extensive configuration leading to performance improvement in many areas of interest. Higher layer analytics like data from IP traffic prediction can assist in the process of allocating resources at the optical layer. This way, light connections are established more efficiently while targeting specific performance goals. For that purpose, an algorithm is designed and evaluated that exploits traffic prediction of data transfers between nodes of an optical metro or backbone network. Next, it utilizes adaptive functionality based on particle swarm optimization to find paths with available spectrum resources. These resources can facilitate more efficiently the future traffic demand, since traffic prediction data are considered when finding the related paths. The innovative resource allocation method is evaluated using small and very large real topologies. It scales (in execution time and resource usage) according to node increase, executes in feasible time frames, and reduces transponder utilization resulting to increased energy efficiency. [ABSTRACT FROM AUTHOR]

SUMMARY Flocking algorithms for a multiagent system are distributed algorithms that only have simple rules for each agent but generate complex formational movement. These algorithms are known as swarm intelligence and are robust and disaster tolerant for most cases. We consider that flocking algorithms that have these characteristics are the way to generate homeostasis in a system. We expect that by making use of this algorithm the system can tune its self-parameters and thus maintain a high performance. First, to apply a flocking algorithm to a system, we extended the flocking algorithm to form an arbitrary lattice for further flexibility. We then applied the extended flocking algorithm to position tracking camera system as an example. [ABSTRACT FROM AUTHOR]

Novel adaptive PSO (particle swarm optimization) algorithms called OPSO (online PSO) and ϵPSO are proposed. These algorithms are designed to prevent increasing of calculation cost in order to embed into online systems, and they have high adaptive performances to environmental changes. The efficiency of the proposed PSO algorithms are demonstrated by numerical simulation and the online identification of an actual dynamic system. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(7): 10-18, 2012; Published online in Wiley Online Library (). DOI 10.1002/ecj.11391 [ABSTRACT FROM AUTHOR]

The behavior of the particle swarm optimization (PSO) algorithm is analyzed by regarding its dynamics as a system with multiplicative noise and applying control-theoretic analysis methods. In order to evaluate the convergence and diversity of the PSO algorithm, two new measures related to the decay rate and l2 gain of the PSO dynamics are introduced. These measures are characterized by linear matrix inequalities and are therefore efficiently computed by convex optimization tools. Numerical experiments suggest that the measures are effective enough to evaluate the convergence and diversity of the PSO algorithm, which can lead to better understanding of the PSO algorithm from the viewpoints of exploitation and exploration abilities. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [ABSTRACT FROM AUTHOR]

Multi-document summarization is a process of automatic creation of a compressed version of a given collection of documents that provides useful information to users. In this article we propose a generic multi-document summarization method based on sentence clustering. We introduce five clustering methods, which optimize various aspects of intra-cluster similarity, inter-cluster dissimilarity and their combinations. To solve the clustering problem a modification of discrete particle swarm optimization algorithm has been proposed. The experimental results on open benchmark data sets from DUC2005 and DUC2007 show that our method significantly outperforms the baseline methods for multi-document summarization. [ABSTRACT FROM AUTHOR]