Swarm Intelligence Optimization Techniques Research Articles

Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network

Introduction: This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path. Method: The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACODijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB. Results: At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED. Conclusion: In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).

Maximizing the output power of magnetically geared generator in low-speed applications using subdomain modeling and particle swarm optimization

This study presents an approach to optimizing the output power in a magnetically geared generator by employing subdomain modeling and particle swarm optimization. Accordingly, the subdomain analytical method is utilized to obtain the induced voltage, inductance which are used to predict output power. Afterward, the swarm intelligence optimization technique is applied to reach the maximum output power point. The 12/17/15 prototype evaluated the proposed model by comparing the calculated results and finite element method simulation.

Fractal Geometry Meets Computational Intelligence: Future Perspectives.

Characterizations in terms of fractals are typically employed for systems with complex and multiscale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many interacting subunits. The human brain can be modeled in terms of observable variables together with their spatio-temporal-functional relations. Computational intelligence is a research field bridging many nature-inspired computational methods, such as artificial neural networks, fuzzy systems, and evolutionary and swarm intelligence optimization techniques. Typical problems faced by means of computational intelligence methods include those of recognition, such as classification and prediction. Although historically conceived to operate in some vector space, such methods have been recently extended to the so-called nongeometric spaces, considering labeled graphs as the most general example of such patterns. Here, we suggest that fractal analysis and computational intelligence methods can be exploited together in neuroscience research. Fractal characterizations can be used to (i) assess scale-invariant properties and (ii) offer numeric, feature-based representations to complement the usually more complex pattern structures encountered in neurosciences. Computational intelligence methods could be used to exploit such fractal characterizations, considering also the possibility to perform data-driven analysis of nongeometric input spaces, therby overcoming the intrinsic limits related to Euclidean geometry.

Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Research on automatic matching of online mathematics courses and design of teaching activities based on multiobjective optimization algorithm.

The teaching of the optimization algorithm is a new kind of swarm intelligence optimization technique, which is superior in optimizing many simple functions. Still, it is not evident in processing some complex problems (group and teaching classification). Achieving automatic matching and knowledge transfer in online courses is imperative in mathematics education. This study proposes a design scheme MTCBO-LR (multiobjective capability optimizer-logistic regression), based on multitask optimization, which enables precise knowledge transfer and data interaction among many educators. It incorporates the standard TLBO algorithm to optimize, provides a variety of learning tactics for students at different stages of mathematics instruction, and is capable of adaptively adjusting these strategies in response to actual teaching needs. Experimental results on various datasets reveal that the proposed method enhances searchability and group diversity in various optimization extremes and outperforms similar methods in resolving to multitask teaching problems.

Swarm-Intelligence Optimization Method for Dynamic Optimization Problem

In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s t-distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references.

Swarm-Based Extreme Learning Machine Models for Global Optimization

Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration, and generalization ability. While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there is no certainty that the arrangement of weights and biases within the hidden layer is optimal. To solve this problem, the traditional ELM has been hybridized with swarm intelligence optimization techniques. This paper displays five proposed hybrid Algorithms “Salp Swarm Algorithm (SSA-ELM), Grasshopper Algorithm (GOA-ELM), Grey Wolf Algorithm (GWO-ELM), Whale optimization Algorithm (WOA-ELM) and Moth Flame Optimization (MFO-ELM)”. These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data. The proposed models applied to the predication of electricity loading data, that describes the energy use of a single residence over a four-year period. In the hidden layer, Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM. The best weights and preferences were calculated by these algorithms for the hidden layer. Experimental results demonstrated that the proposed MFO-ELM achieved 98.13% accuracy and this is the highest model in accuracy in tumor type classification gene expression data. While in predication, the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript

Alzheimer’s dementia (AD) affects memory, language, and cognition which worsens over time. It is critical to develop a reliable early detection method before permanent brain atrophy and cognitive impairment. This study uses clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer’s patients. This audio transcript data is taken from DementiaBank which is the largest public dataset of AD transcripts. The study aims to show how transfer learning-based models and swarm intelligence optimization techniques can be used to predict Alzheimer’s disease. To enhance the prediction performance for Alzheimer’s disease, a hybrid swarm intelligence linguistic feature selection (HSI-LFS) approach is proposed that extracts a combined feature set using Particle Swarm Optimization (PSO), Dragonfly Optimization (DO), and Grey Wolf Optimization (GWO) algorithms. In addition, a transfer learning-based model called HSI-LFS-BERT, a combination of the HSI-LFS feature selection method and Bidirectional Encoder Representations from Transformer (BERT) algorithm is proposed. The proposed model is compared using two feature sets: first set consists of the initial feature set and the second set contains hybrid feature set that has been extracted using the suggested HSI-LFS method. The BERT embedding with HSI-LFS outperformed the conventional feature set providing the most accurate modeling parameters while reducing the computations by 27.19%. The proposed HSI-LFS-BERT model excelled state-of-the-art models achieving 98.24% accuracy, 91.56% precision, and 98.78% recall.

Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

Major developments in relevant technology make the advanced driver assistance systems and autonomous driving functions more attainable. Thus, conventional practices being applied in vehicle production evolves towards highly automated, safer, and more comfortable vehicles. Although advanced driver assistance systems and autonomous driving functions have many advantages, such as enhanced driver convenience, increased comfort, and fuel economy; concerns related to safety still exist. For instance, failures related to sensors or algorithms being used can lead to critical problems. Therefore, controller algorithms should be more robust and well-optimized in order to eliminate these safety issues. This requires the implementation of automated optimization algorithms for the controller parameter tuning process. The main objective of this study is to optimize the designed controller for an adaptive cruise control system by using the particle swarm optimization method, which is a swarm intelligence optimization technique. Based on the results, it is observed that the use of automated optimization techniques for adaptive cruise control systems leads to better accuracy and safety for driving control. Furthermore, the time consumed for the controller parameter tuning process is also decreased significantly. In conclusion, the adaptive cruise control system requirements can be easily and accurately ensured by the use of particle swarm optimization algorithm.

A clustering based Swarm Intelligence optimization technique for the Internet of Medical Things

Internet of Medical Things (IoMT) is a recently introduced paradigm which has gained relevance as an emerging technology for widely connected and heterogeneous networks. In the medical context, these networks involve many different processes run by different types of devices called objects that interact and collaborate to achieve a common goal (e.g, diagnosis, treatment, monitoring, or rehabilitation of a patient). An IoMT framework in a smart healthcare system dynamically monitors patients to respond to their assistance demands so that vital signs of critical or unusual cases can be uncovered based on the collected data. To this end, an effective technique called SIoMT (Swarm Intelligence optimization technique for the IoMT) is proposed in this paper for periodically discovering, clustering, analyzing, and managing useful data about potential patients. Notably, the SIoMT technique is widely used with distributed nodes for analyzing and managing data groups. Different from the existing clustering algorithms, SIoMT performs clustering based on the characteristics and distance between objects or swarms. More specifically, these data are collected and grouped, in early stage, using a clustering approach inspired by the Bee Colony Optimization algorithm (BCO), adopting some standard quality measures which helped minimizing the latency and required computational cost. To test the performance of the proposed SIoMT, one public dataset (Ward2ICU) was considered from the online source. Various experiments were done to analyze the effects of different parameters on the proposed SIoMT’s performance, and the results from the final variant of the proposed algorithm were compared against different variants of the same algorithm with different clustering algorithms and different optimization algorithms. Subsequently, after analyzing different components by solving various IoMT datasets, the capability and the superiority of the proposed SIoMT approach is well-established among its competitive counterparts.

Kinetic parameters estimation via dragonfly algorithm (DA) and comparison of cylindrical and spherical reactors performance for CO2 hydrogenation to hydrocarbons

Climate change and global warming, as well as growing global demand for hydrocarbons in industrial sectors, make great incentives to investigate the utilization of CO2 for hydrocarbons production. Therefore, finding an in-depth understanding of the CO2 hydrogenation reactors along with simulating reactor responses to different operating conditions are of paramount importance. However, the reaction mechanisms for CO2 hydrogenation and their corresponding kinetic parameters have been disputable yet. In this regard, considering the previously proposed Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism, which considered CO2 hydrogenation as a combination of reverse water gas shift (RWGS) and Fischer-Tropsch (FT) reactions, and using a one-dimensional pseudo-homogeneous non-isothermal model, kinetic parameters of the rate expressions are estimated via fitting experimental and modelling data through a novel swarm intelligence optimization technique called dragonfly algorithm (DA). The predicted reactants conversion using DA algorithm are closer to the experimental data (with about 4% error) comparing to those obtained by the artificial bee colony (ABC) algorithm, and are in significant agreement with available literature data. The proposed model is used to assess the effect of reactor configuration on the performance and temperature fluctuations. Results show that axial flow spherical reactor (AFSR) and radial flow spherical reactor (RFSR) exhibiting the same surface area with that of the cylindrical reactor (CR), i.e., AFSR-2 and RFSR-2-i are the most efficient exhibiting hydrocarbons selectivity of 40.330% and 40.286% at CO2 conversion of 53.763% and 53.891%. In addition, it is revealed that the location of the jacket has an essential role in controlling the reactor temperature.

Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis

BackgroundIn psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin’s surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging. However, for accurate disease diagnosis and severity detection, lesion segmentation has prime importance. In that context, our group had previously performed psoriasis lesion segmentation using the conventional clustering algorithm. However, it suffers from the constraint of falling into the local sub-optimal centroids of the clusters. ObjectiveThe main objective of this paper is to implement an optimal lesion segmentation technique with aims at global convergence by reducing the probability of trapping into the local optima. This has been achieved by integrating swarm intelligence based algorithms with conventional K-means and Fuzzy C-means (FCMs) clustering algorithms. MethodologyThere are a total of eight different suitable combinations of conventional clustering (i.e., K-means and Fuzzy C-means (FCMs)) and four swarm intelligence (SI) techniques (i.e., seeker optimization (SO), artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO)) have been implemented in this study. The experiments are performed on the dataset of 780 psoriasis images from 74 patients collected at Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. In this study, we are employing swarm intelligence optimization techniques in combination with the conventional clustering algorithms to increase the probability of convergence to the optimal global solution and hence improved clustering and detection. ResultsThe performance has been quantified in terms of four indices, namely accuracy (A), sensitivity (SN), specificity (SP), and Jaccard index (JI). Among the eight different combinations of clustering and optimization techniques considered in this study, FCM + SO outperformed with mean JI = 0.83, mean A = 90.89, mean SN = 92.84, and mean SP = 88.27. FCM + SO found statistical significant than other approaches with 96.67 % of the reliability index. ConclusionThe results obtained reflect the superiority of the proposed techniques over conventional clustering techniques. Hence our research development will lead to an objective analysis for automatic, accurate, and quick diagnosis of psoriasis.

Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

Harris hawks optimization (HHO) is a new swarm intelligence optimization technique. Because of its simple structure and easy to implement, HHO has attracted research interest from scholars in different fields. However, the low population diversity and the single search method in the exploration phase weakened the global search capability of the HHO algorithm. In response to these defects, this paper proposes an improved HHO algorithm based on adaptive cooperative foraging and dispersed foraging strategies. First, the adaptive cooperative foraging strategy uses three random individuals to guide the position update, which achieves cooperation between individuals. Then the cooperation behavior is embedded in the one-dimensional update operation framework, and the one-dimensional or total-dimensional update operation is adaptively selected. This way allows the algorithm to perform position update operations for a specific dimension of individual vectors with a certain probability, which improves the population diversity. Second, the dispersed foraging strategy is introduced into the HHO, forcing a part of Harris hawks to leave their current position to find more prey to obtain a better candidate solution. This way effectively avoids the algorithm falling into local optimum. Finally, a randomly shrinking exponential function is used to simulate the energy change of the prey, so that the algorithm maintains the exploration ability in the later exploitation process, effectively balancing the exploration and exploitation ability of the algorithm. The performance of the proposed ADHHO algorithm is evaluated using Wilcoxon's test on unimodal, multimodal and CEC 2014 benchmark functions. Numerical results and statistical experiments show that ADHHO provides better solution quality, convergence accuracy and stability compared with other state-of-the-art algorithms.

Swarm Intelligence-Based Optimization Techniques for Dynamic Response and Power Quality Enhancement of AC Microgrids: A Comprehensive Review

The increasing penetration of Microgrids (MGs) into existing power systems and “plug and play” capability of Distributed Generators (DGs) causes large overshoots and settling times along with various power quality issues such as voltage and frequency flickers, current harmonics and short current transients. In this context, over the past few years, considerable research has been undertaken to investigate and address the mentioned issues using different control schemes in conjunction with soft computational techniques. The recent trends and advancements in the field of Artificial Intelligence (AI) have led the development of Swarm Intelligence (SI) based optimized controllers for smooth Renewable Energy Sources (RES) penetration and optimal voltage, frequency, and power-sharing regulation. Moreover, the recent studies have proved that the SI-based controllers provide enhanced dynamic response, optimized power quality and improved the dynamic stability of the MG systems as compared to the conventional control methods. Their importance in modern AC MG architectures can be judged from the growing number of publications in the recent past. However, literature, pertaining to SI applications to AC MG, is scattered with no comprehensive review on this significant development. As such, this study provides an overview of 15 different SI optimization techniques as applied to AC MG controls from 43 research publications including a detailed review of one of the elementary and most widely used SI based metaheuristic optimization algorithms called Particle Swarm Optimization (PSO) algorithm. This comprehensive review provides a valuable one-stop source of knowledge for the researchers and experts working on SI controller's applications for AC MG dynamic response and power quality improvements.

Greenfly Aphid Swarm Optimization Algorithm

A new metaheuristic swarm intelligence optimization technique, called general greenfly aphid swarm optimization algorithm, which is proposed by enhancing the performance of swarm optimization through cockroach swarm optimization algorithm. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, cockroach swarm optimization and grasshopper optimization algorithm. Numerical experiments show that the greenfly aphid swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau’s optimization problem, are also considered and the proposed greenfly aphid swarm optimization algorithm is shown to be competitive in those applications.

Artificial bee colony directive for continuous optimization

The artificial bee colony (ABC) algorithm, a relatively new swarm intelligence optimization technique, has been shown to be a competitive alternative to other population-based algorithms. This paper fundamentally modifies the solution search equations of the ABC in a manner that sends bee agents in search of three types of search regions that improve convergence speeds and proposes an innovative artificial bee colony directive (ABCD) algorithm. Moreover, this paper validates the ABCD algorithm by showing better performance by improving two familiar ABC variants in experimental tests. In addition, 10 applicable search strategies that adopt the proposed three search-region types are presented. The proposed ABCD not only improves the original ABC and its subsequently improved versions but is also useful for setting the search regions of other swarm intelligence algorithms.

Artificial Coronary Circulation System; A new bio-inspired metaheuristic algorithm

A new swarm intelligence optimization technique is proposed, called Artificial Coronary Circulation System (ACCS). This optimization method simulates the coronary arteries (veins) growth on human heart. In this algorithm, each capillary is considered as a candidate solution. This algorithm starts with a random initial population of candidate solutions, and by using Coronary Growth Factor (CGF) evaluates the solutions. In each run the best candidate solution is selected as the main coronary vessel (artery or vein) and the other capillaries are considered as searchers of the search space. Then the heart decides other candidates to move toward/away from the main coronary vessels and searches for the optimal solution by using the heart memory. Finally, application of the proposed algorithm is demonstrated using some benchmark functions and some mechanical problems, confirming the potential and capability of the new algorithm.

A Random Opposition-Based Learning Grey Wolf Optimizer

Grey wolf optimizer (GWO) algorithm is a swarm intelligence optimization technique that is recently developed to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. It has been successfully applied to many real world applications. In the GWO algorithm, “C”is an important parameter which favoring exploration. At present, the researchers are few study the parameter “C”in GWO algorithm. In addition, during the evolution process, the other individuals in the population move towards to the α, β, and δ wolves which are to accelerate convergence. However, GWO is easy to trap in the local optima. This paper presents a modified parameter “C”strategy to balance between exploration and exploitation of GWO. Simultaneously, a new random opposition-based learning strategy is proposed to help the population jump out of the local optima. The experiments on 23 widely used benchmark test functions with various features, 30 benchmark problems from IEEE CEC 2014 Special Session, and three engineering design optimization problems. The results reveal that the proposed algorithm shows better or at least competitive performance against other compared algorithms on not only global optimization but also engineering design optimization problems.

A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms

Due to the tremendous increment of data on the web, extracting the most important data as a conceptual brief would be valuable for certain users. Therefore, there is a massive enthusiasm concerning the generation of automatic text summary frameworks to constitute abstracts automatically from the text, web, and social network messages associated with their satellite content. This survey highlights, for the first time, how the swarm intelligence (SI) optimization techniques are performed to solve the text summarization task efficiently. Additionally, a convincing justification of why SI, especially Ant Colony Optimization (ACO), has been presented. Unfortunately, three types of text summarization tasks using SI indicate bit utilizing in the literature when contrasted with the other summarization techniques as machine learning and genetic algorithms, in spite of the fact that there are seriously promising outcomes of the SI methods. On the other hand, it has been noticed that the summarization task with multiple types has not been formalized as a multi-objective optimization (MOO) task before, despite that there are many objectives which can be considered. Moreover, the SI was not employed before to support the real-time summary approaches. Thus, a new model has been proposed to be adequate for achieving many objectives and to satisfy the real-time needs. Eventually, this study will enthuse researchers to further consider the various types of SI when solving the summarization tasks, particularly, in the short text summarization (STS) field.