Advances in Swarm Intelligence

The application of swarm intelligence (SI) in the optimization field has been gaining much popularity, and various SI algorithms have been proposed in last decade. However, with the increased number of SI algorithms, most research focuses on the implementation of a specific choice of SI algorithms, and there has been rare research analyzing the common features among SI algorithms coherently. More importantly, no general principles for the implementation and improvement of SI algorithms exist for solving various optimization problems. In this research, aiming to cover such a research gap, a unified framework towards SI is proposed inspired by the in-depth analysis of SI algorithms. The unified framework consists of the most frequently used operations and strategies derived from typical examples of SI algorithms. Following the proposed unified framework, the intrinsic features of SI algorithms can be understood straightforwardly and the implementation and improvement of SI algorithms can be achieved effortlessly, which is of great importance in practice. The numerical experiments examine the effects of the possible strategies employed in the unified framework, and provide pilot attempts to validate the performance of different combinations of strategies, which can not only facilitate specific SI algorithm application, but also can motivate SI algorithm innovation.

Swarm intelligence for next-generation networks: Recent advances and applications

Water governance involves many stakeholders to effectively implement the service. Poor water governance can lead to water scarcity, pollution, and other critical issues in economic adjustment. Poor water governance can even lead to microbial threats to humans. However, such difficulties can be overcome with the inclusion of data-driven systems. Assessing the risk of infection and disease caused by microbial contamination of drinking water in urban and rural areas is an important concern from the perspective of researchers and stakeholder groups involved in water governance. The current threats of biological warfare and terrorism, the resurgence of diseases like tuberculosis, and recent deaths from microbial contamination outbreaks, especially in foods and in water and air, have highlighted the significance of prompt and precise assessments of microbial contamination. To address these concerns, healthcare representatives conduct periodic surveys across various locations to collect health-related statistics linked to water pollution. These surveys generate extensive datasets, which are then submitted to medical units at the block or district levels for further analysis. However, managing and analyzing such a vast amount of geographically distributed data poses a significant challenge. This is where artificial intelligence (AI) can play a crucial role in enhancing the efficiency and accuracy of water governance systems. The generated datasets can be a suitable source for developing AI-based software architectures that can detect and mitigate microbial threats in specific regions. However, since datasets are made available in different geographically separated regions, establishing communication through network-based protocols is an important concern for the effective analysis of critical scenarios. One promising approach to addressing these challenges is the integration of microservice design patterns and swarm intelligence. Microservices offer a decentralized and scalable solution, allowing various components of a water governance system to function independently while remaining interconnected. Swarm intelligence, inspired by the collective behavior of decentralized systems such as ant colonies, can be utilized to optimize decision-making and resource allocation in dynamic environments. The integration of microservice design patterns and swarm intelligence can play a crucial role in providing effective solutions to stakeholders involved in water governance. We call it MicroAI (Microservice-based artificial intelligence). To validate the effectiveness of MicroAI, a quality evaluation framework will be developed, assessing key performance metrics such as accuracy, scalability, and response time. Additionally, statistical analysis will be conducted to establish the hypothesis that MicroAI outperforms existing deployment methodologies. By leveraging the deployment power of microservices and swarm intelligence, it can be concluded that MicroAI has the potential to revolutionize water governance, ensuring safer and more sustainable water management practices for the future. In this chapter, we present an architecture to deploy loosely coupled microservices with swarm intelligence. The quality evaluation framework of MicroAI will be evaluated to validate the proposed method of architecture. The statistical analysis will be conducted to establish the hypothesis that MicroAI is superior to other deployment methodologies. Overall, it can be concluded that MicroAI can enhance water governance by analyzing microbial threats using AI-driven datasets, ensuring real-time communication and improved decision-making.

In 1989 Gerardo Beni and Jing Wang first proposed the name ―Swarm Intelligence‖ in their paper ―Swarm Intelligence in Cellular Robotic Systems‖. Some remarkable observations of different researchers are studied in this paper, like the proximity principle, the quality principle, the principle of diverse response, the principle of stability, the principle of adaptability. To enhance the capabilities of robot and different systems, researchers started to exploit the behavior of natural systems. Swarm groups are governed by three rules, move in the same direction as your neighbor, remain close to your neighbor, and avoid collision with your neighbor .Characteristics of swarm groups are emergence and stigmergy. Different insects like ants, wasps, termites carry out a work locally for global goal with sufficient flexibility as they are not controlled centrally. In this paper the existing research works are analysed to show the behavior in social insects by using self-organization, positive feedback, negative feedback, amplification of fluctuation, multiple interactions. It has also been observed that these insects are almost blind and memoryless, still they communicate indirectly among themselves for stigmergic effect by using pheromone. Implementation of swarm intelligence in robotics i.e., swarm robots are narrated. The limitations of swarm robots as well as factors behind the success of swarm robotics have also been encompassed. Finally authors focus on swarm robots applications in telecommunication fields, civil engineering and digital image processing.

This book and its companion volumes, LNCS volumes 9140, 9141 and 9142, constitute the proceedings of the 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the Second BRICS Congress on Computational Intelligence, CCI 2015, held in Beijing, China in June 2015. The 161 revised full papers presented were carefully reviewed and selected from 294 submissions. The papers are organized in 28 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development, such as novel swarm-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid methods; multi-objective optimization; multi-agent systems and swarm robotics; Neural networks and fuzzy methods; data mining approaches; information security; automation control; combinatorial optimization algorithms; scheduling and path planning; machine learning; blind sources separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems; forecasting algorithms; classification; tracking analysis; simulation; image and texture analysis; dimension reduction; system optimization; segmentation and detection system; machine translation; virtual management and disaster analysis

Swarm intelligence, inspired by the collective behavior observed in social organisms, has emerged as a powerful paradigm in both natural and artificial systems. The concept of a swarm refers to a large group of simple agents that interact locally with one another and their environment, giving rise to complex and intelligent behavior at the group level. Swarm intelligence, on the other hand, represents the ability of a swarm to self-organize, adapt, and solve complex problems without central control. In nature, swarms of social insects such as bees, ants, termites, and birds exhibit remarkable abilities in foraging, navigation, resource allocation, and defense. These organisms demonstrate how the interactions of simple individuals can lead to efficient and robust solutions to various challenges faced in their environments. In artificial systems, researchers have successfully translated the principles of swarm intelligence into algorithms and techniques for optimization, decision-making, and problem-solving. Popular swarm intelligence algorithms, such as Ant Colony Optimization, Particle Swarm Optimization, and Artificial Bee Colony, have shown great promise in tackling complex optimization and search tasks. This paper provides an overview of the fundamental concepts of swarm intelligence and explores the similarities and differences between natural and artificial swarms. It delves into the principles of self-organization, decentralized decision-making, and adaptation that underpin swarm intelligence, allowing these systems to cope with dynamic and uncertain environments. Furthermore, the paper examines the application domains of swarm intelligence, ranging from robotics and autonomous systems to data clustering, image processing, and network routing. The potential of swarm robotics in solving real-world challenges, such as environmental monitoring, disaster response, and precision agriculture, is also explored. Swarm intelligence presents a compelling avenue for understanding and harnessing emergent collective behavior in both biological and computational contexts. The interplay of simplicity, local interactions, and adaptation enables swarms to tackle complex problems efficiently, making them a valuable source of inspiration for the design of intelligent systems in various fields. The study of swarm intelligence continues to advance, offering exciting possibilities for creating adaptive, robust, and scalable solutions in the ever-evolving landscape of artificial intelligence and beyond.

The modeling of emergent swarm intelligence constitutes a major challenge and it has been tacked in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individuals determine their behaviors based on their internal contradictions whilst they associate and in-teract to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the development of individuals’ internal contradictions and the interactions taking place between in-dividuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configu-rations of individuals’ internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applica-bility. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.

Fuzzy systems (FSs) have been extensively applied to automatic control, pattern recognition, and decision analysis. However, a common bottleneck is encountered in the derivation of fuzzy rules, which is often difficult, time consuming, and relies on expert knowledge. To automate the design of FSs, many metaheuristic learning algorithms have been proposed. One major optimization category uses Swarm Intelligence (SI) model (Kennedy et al., 2001). The SI technique studies collective behavior in decentralized systems. Its development was based on mimicking the social behavior of animals or insects in an effort to find the optima in the problem space. SI models are initialized with a population of random solutions. One well-known SI model is particle swarm optimization (PSO) (Kennedy & Eberhart, 1995). Many modified PSO models have been proposed and successfully applied to different optimization problems (Clerc & Kennedy, 2002; Bergh & Engelbrecht, 2004; Ratnaweera et al., 2004; Juang, 2004; Kennedy & Mendes, 2006; Parrott & Li, 2006; Chen & Li, 2007). FS design using PSO has also been proposed in several studies (Juang, 2004; Chatterjee et al., 2005; Juang et al., 2007; Araujo & Coelho, 2008; Sharma et al., 2009). Another well-known SI is ant colony optimization (ACO) (Dorigo & Stutzle, 2004). The ACO technique is inspired by real ant colony observations. It is a multi-agent approach that was originally proposed to solve difficult discrete combinatorial optimization problems, such as the traveling salesman problem (TSP) (Dorigo et al., 1996; Dorigo & Gambardella, 1997). In the original ACO meta-heuristic, artificial ant colonies cooperate to find good solutions for difficult discrete optimization problems. Different ACO models have been applied to FS design problems (Cassillas et al., 2000; Cassillas et al., 2005; Mucientes & Casillas; 2007; Juang & Lo, 2007; Juang et al., 2008; Juang & Lu; 2009). In (Cassillas et al.,2000; Mucientes & Casillas; 2007; Juang et al., 2008; Juang & Lu; 2009), the FS input space was partitioned in grid type with antecedent part parameters of an FS manually assigned in advance. In (Juang & Lo, 2007), the FS input space was flexibly partitioned using a fuzzy clustering-like algorithm in order to reduce the total number of rules. For all of these studies, the consequent part parameters were optimized in discrete space using ACO. Since only the consequent part parameters are optimized, and the optimization space is restricted to be discrete, the designed FSs are unsuitable for problems where high accuracy is a major concern. Source: Fuzzy Systems, Book edited by: Ahmad Taher Azar, ISBN 978-953-7619-92-3, pp. 216, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

This work analyses the performance of Reinforcement Learning (RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area coverage. It builds upon previous work which looked at various elements of both algorithms. The main aim of this paper is to address the continuous state-space challenge within this work by using partitioning to manage the high dimensionality problem. This enabled comparing the performance of the classical cases of both RL and SI establishing a baseline for future comparisons of improved versions. From previous work, SI was observed to perform better across various key performance indicators. However, after tuning parameters and empirically choosing suitable partitioning ratio for the RL state space, it was observed that the SI algorithm still maintained superior coordination capability by achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. Though the RL technique showed better average peak user coverage, the unpredictable coverage dip was a key weakness, making SI a more suitable algorithm within the context of this work.

PurposeIt is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.Design/methodology/approachA structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.FindingsThe paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.Originality/valueThe paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

Swarm intelligence and the neural network have been given considerable attention by researchers. As just a motivation from the biological evolution of the human neuron system, a neural network further defined as an ANN (artificial neural network) has become the computational intelligence power that is used for fixing complex problems. A further strategy influenced by artificial intelligence seems to be swarm intelligence, which primarily deals instead with natural and artificial systems consisting of multiple individuals and groups coordinated through self-organization and decentralized responsibility. Everything just consists of a sequence of intelligent entities that arrive as collective wisdom that's already beginning to emerge. This whole chapter covers primarily a thorough analysis of swarm intelligence, neural networks and their roles and applications. We have presented a comprehensive review of existing algorithms of swarm intelligence that produce positive outcomes in numerous sectors i.e., Firefly method, Bat method, Lion optimization method, Spider Monkey optimization method, Chicken swarm optimization method. With today's fast-growing traffic availability, congestion has just become a significant issue in several cities around the world. This whole chapter provides the review of various research work that offers both the road traffic but also offers the citizen a substitute, uncongested path for their transportation; thus, this helps to save time and energy but also prevent pollution. AI-based swarm intelligence, IoT and neural network–based deep learning are mainly covered. An embedded system involves utilizing swarm intelligence and captures images and videos of the traffic on the road and statistical analysis of their behavior according to the surrounding environment and reroutes the traffic in various routes so congestion for each path will be reduced.

Swarm intelligence has been applied to form-finding methodologies in architectural design, with Processing as one of the most important working platforms deployed. With swarm intelligence feedback is one of the core issues to deal with as this is where complexity emerges and the potential of this methodology becomes manifest. However, with Processing only limited feedback can be achieved as true swarm intelligence involves other incalculable and nonlinear factors, such as stigmergy, that requires a deep relationship not only with other agents but also with the broad background condition. As such, we need to draw a distinction between computational and non-computational complexity, and to recognise the limitations of computational complexity. Processing therefore offers us only a computational and therefore a limited model for understanding swarm intelligence.

Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In particular, the discipline focuses on the behavior of social insects such as fish schools and bird flocks and colonies of ants, termites, bees, and wasps. The most well-known examples of systems studied by swarm intelligence are particle swarm optimization (PSO) and ant colony optimization (ACO). Particle swarm optimization mimics the behavior of fish schooling and bird flocking. PSO is a population-based stochastic optimization strategy with fast convergent speed than general evolutionary algorithms (EAs). Different from EAs, each particle employs not only position information but also the velocity information. They communicate good positions to each other and adjust their own positions according to their decision. In PSO, a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually, the swarm as a whole, like a flock of birds collectively foraging for food, moves close to an optimum of the fitness function. Ant colony optimization is a probabilistic technique for solving computational hard problems which can be reduced to finding optimal paths. The main idea is the indirect communication between the ants by means of chemical pheromone trails. The special issue on the swarm intelligence is dedicated to the latest work in the theory and applications in this exciting area. Our aim is to provide a useful reference for understanding new trends on swarm intelligence. After a detailed review process, a total of four papers were selected to reflect the call thematic vision. The contents of these studies are briefly described as follows. The first paper entitled ‘Analysis of emergent symmetry breaking in collective decision making’ by Heiko Hamann, Thomas Schmickl, Heinz Worn, and Karl Crailsheim investigates a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent– agent encounters and is inspired by the aggregation behavior of honey bees. In this article, symmetry breaking is defined, several symmetry-breaking measures are proposed, and phenomenon of emergent symmetry breaking within authors’ observed system is reported. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed, and two macroscopic features are determined that significantly influence the symmetry-breaking behavior. In addition, the connection between the ability of the Z. Cui (&) Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024 Shanxi, Taiyuan, China e-mail: cuizhihua@gmail.com

The healthcare industry is rapidly developing across the world. The healthcare industry generates a large volume of diverse data. It is crucial for healthcare sectors to effectively extract, collect, and exploit data. Swarm intelligence algorithms have been applied on the data in detecting cancer, heart disease, malignancies, and cardiology prognosis. Disease diagnosis and treatment have benefited from swarm intelligence techniques. In terms of techniques and outcomes, this chapter examines several uses of swarm intelligence with data prediction techniques using social media data in healthcare. The chapter will cover different areas where prediction techniques will be applied to the data collected by different social media platforms, like data mining, risk scoring of illnesses, and early recognition of patients' condition deterioration using data detection that will use algorithms based on swarm intelligence.

The mobile ad hoc networks gained its popularity for its various applications, quick and easy deployment. This network does not require any fixed infrastructure for deployment. Mobile nodes in this network communicate with each other through a wireless communication medium and makes this network highly vulnerable to many attacks. One of the well-known attack is packet drop attack. Whenever the network faces any kind of attack suddenly degrades network performance. In this paper, we have designed a protocol known as cross-layer packet drop attack detection using swarm intelligence (CLPDM-SI). This protocol followed a cluster based collective swarm intelligence detection mechanism to find a malicious node in real data acquisition system which undergoes packet drop attack. Our protocol (CLPDM-SI) is compared with a protocol which does not follow swarm intelligence known as Adapting Cross-Layer Approach for Detecting and Segregating Malicious Nodes (ACLDSM) mechanisms. Our comparison is done based on various QoS parameters delay, throughput, packet delivery ratio and false positive detection in the MAC layer. Our result shows significant improvements after incorporating the swarm intelligence. By inspecting CPU and memory utilization time, the algorithm finds the false positive of suspected malicious nodes.