Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

Swarm Intelligence Based Routing with Black Hole Attack Detection in MANET

Mobile Ad hoc Network(MANET)possesses unique characteristics which makes it vulnerable to security threats.In MANET,it is highly challenging to protect the nodes from cyberattacks.Power conservation improves both life time of nodes as well as the network.Computational capabilities and memory constraints are critical issues in the implementation of cryptographic techniques.Energy and security are two important factors that need to be considered for improving the performance of MANET.So,the incorporation of an energy efficient secure routing protocol becomes inevitable to ensure appropriate action upon the network.The nodes present in a network are limited due to energy constraints and secure communication protocols.Hence,the current study proposed an energy-efficient defense scheme using swarm intelligence approach.The functioning of the proposed method was validated under NS2 simulation.The experimental results confirmed that the proposed work outperformed existing methods in terms of packet delivery ratio,average end-to-end delay and throughput.

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

As millimeter waves will be widely used in the Internet of Things(IoT)and Telematics to provide high bandwidth communication and mass connectivity,the coverage optimization of base stations can effectively improve the quality of communication services.How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers.This paper proposes the Muti-Fusion Sparrow Search Algorithm(MFSSA)optimize the situation to address the problem of discrete coverage maximization and rapid convergence.Firstly,the initial swarm diversity is enriched using a sine chaotic map,and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability.Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a“precocious”state.Such a swarm is very suitable for solving NP-hard problems.Secondly,an elite opposition-based learning strategy is added to expand the search range of the algorithm,and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum.This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm.Finally,the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem,and the convergence speed is better than other algorithms.MFSSA is improved by more than 10%compared to the original algorithm.

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

A new-style clustering algorithm based on swarm intelligent theory

Traditional clustering algorithms generally have some problems, such as the sensitivity to initializing parameter, difficulty in finding out the optimization clustering result and the validity of clustering. In this paper, a FSM and a mathematic model of a new-style clustering algorithm based on the swarm intelligence are provided. In this algorithm, the clustering main body moves in a three-dimensional space and has the abilities of memory, communication, analysis, judgment and coordinating information. Experimental results conform that this algorithm has many merits such as insensitive to the order of the data, capable of dealing with exceptional, high-dimension or complicated data. The algorithm can be used in the fields of Web mining, incremental clustering. economic analysis, oattern recognition, document classification and so on.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

Swarm intelligence optimization and its application in geophysical data inversion

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems： （1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends

Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

SWARM INTELLIGENCE BASED DYNAMIC REAL-TIME SCHEDULING APPROACH FOR SEMICONDUCTOR WAFER FAB

Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling for semiconductor wafer fab is proposed.The relevant algorithm, pheromone-based dynamic real-time scheduling algorithm (PBDR), is given.MIMAC test bed data set mini-fab is used to compare PBDR with FIFO (first in first out),SRPT(shortest remaining processing time) and CR(critical ratio) under three different release rules,i.e. deterministic rule, Poisson rule and CONWIP (constant WIP). It is shown that PBDR is prior toFIFO, SRPT and CR with better performance of cycle time, throughput, and on-time delivery,especially for on-time delivery performance.

Swarm intelligence for mixed-variable design optimization

Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap-proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN

To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.

Hybrid Swarm Intelligence Based QoS Aware Clustering with Routing Protocol for WSN

Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information processing.The recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain standards.The restrictions and requirements in QoS management completely depend upon the nature of target application.Some of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and throughput.In this scenario,clustering and routing are considered as the most effective techniques to meet the demands of QoS.Since they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be implemented.The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)algorithm.The proposed QoSCRSI technique performs two-level clustering and proficient routing.Initially,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the network.In order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node counts.The experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.

A Swarm Intelligence Networking Framework for Small Satellite Systems

Recent development of technologies and methodologies on distributed spacecraft systems enable the small satellite network systems by supporting integrated navigation, communications and control tasks. The distributed sensing data can be communicated and processed autonomously among the network systems. Due to the size, density and dynamic factors of small satellite networks, the traditional network communication framework is not well suited for distributed small satellites. The paper proposes a novel swarm intelligence based networking framework by using Ant colony optimization. The proposed network framework enables self-adaptive routing, communications and network reconstructions among small satellites. The simulation results show our framework is suitable for dynamic factors in distributed small satellite systems. The proposed schemes are adaptive and scalable to network topology and achieve good performance in different network scenarios.

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

Optimal seismic design of reinforced concrete structures under timehistory earthquake loads using an intelligent hybrid algorithm

A reliable seismic-resistant design of structures is achieved in accordance with the seismic design codes by designing structures under seven or more pairs of earthquake records. Based on the recommendations of seismic design codes, the average time-history responses （ATHR） of structure is required. This paper focuses on the optimal seismic design of reinforced concrete （RC） structures against ten earthquake records using a hybrid of particle swarm optimization algorithm and an intelligent regression model （IRM）. In order to reduce the computational time of optimization procedure due to the computational efforts of time-history analyses, IRM is proposed to accurately predict ATHR of structures. The proposed IRM consists of the combination of the subtractive algorithm （SA）, K-means clustering approach and wavelet weighted least squares support vector machine （WWLS-SVM）. To predict ATHR of structures, first, the input-output samples of structures are classified by SA and K-means clustering approach. Then, WWLS-SVM is trained with few samples and high accuracy for each cluster. 9- and 18-storey RC frames are designed optimally to illustrate the effectiveness and practicality of the proposed IRM. The numerical results demonstrate the efficiency and computational advantages of IRM for optimal design of structures subjected to time-history earthquake loads.

A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch

This paper presents a pooled-neighbor swarm intelligence approach (PNSIA) to optimal reactive power dispatch and voltage control of power systems. The proposed approach uses more particles’ information to control the mutation operation. The proposed PNSIA algorithm is also extended to handle mixed variables, such as transformer taps and reactive power source in- stallation, using a simple scheme. PNSIA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system. Simulation results showed that the proposed approach is superior to current methods for finding the optimal solution, in terms of both solution quality and algorithm robustness.

An Analysis of Foraging and Echolocation Behavior of Swarm Intelligence Algorithms in Optimization: ACO, BCO and BA

Optimization techniques are stimulated by Swarm Intelligence wherever the target is to get a decent competency of a problem. The knowledge of the behavior of animals or insects has a variety of models in Swarm Intelligence. Swarm Intelligence has become a potential technique for evolving many robust optimization problems. Researchers have developed various algorithms by modeling the behaviors of the different swarm of animals or insects. This paper explores three existing meta-heuristic methods named as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) and Bat Algorithm (BA). Ant Colony Optimization was stimulated by the nature of ants. Bee Colony Optimization was inspired by the plundering behavior of honey bees. Bat Algorithm was emerged on the echolocation characteristics of micro bats. This study analyzes the problem-solving behavior of groups of relatively simple agents wherein local interactions among agents, are either directly or indirectly through the environment. The scope of this paper is to explore the characteristics of swarm intelligence as well as its advantages, limitations and application areas, and subsequently, to explore the behavior of ants, bees and micro bats along with its most popular variants. Furthermore, the behavioral comparison of these three techniques has been analyzed and tried to point out which technique is better for optimization among them in Swarm Intelligence. From this, the paper can help to understand the most appropriate technique for optimization according to their behavior.

Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods

An influence game is a simple game represented over an influence graph(i.e.,a labeled,weighted graph)on which the influence spread phenomenon is exerted.Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis,decision-systems,voting systems,and collective behavior.The exact calculation of several of these properties and parameters is computationally hard,even for a small number of players.Two examples of these parameters are the length and the width of a game.The length of a game is the size of its smaller winning coalition,while the width of a game is the size of its larger losing coalition.Both parameters are relevant to know the levels of difficulty in reaching agreements in collective decision-making systems.Despite the above,new bio-inspired metaheuristic algorithms have recently been developed to solve the NP-hard influence maximization problem in an efficient and approximate way,being able to find small winning coalitions that maximize the influence spread within an influence graph.In this article,we apply some variations of this solution to find extreme winning and losing coalitions,and thus efficient approximate solutions for the length and the width of influence games.As a case study,we consider two real social networks,one formed by the 58 members of the European Union Council under nice voting rules,and the other formed by the 705 members of the European Parliament,connected by political affinity.Results are promising and show that it is feasible to generate approximate solutions for the length and width parameters of influence games,in reduced solving time.