Concept Of Swarm Intelligence Research Articles

Проблема приватности в цифровом обществе роевого интеллекта

Introduction. The development of digital technologies requires the specification of the type of the emerging digital society. One of the development scenarios is the formation of a digital society of swarm intelligence. Theoretical analysis. The concept of swarm intelligence means an optimization algorithm that imitates the behavior of swarms or colonies of insects and bird flocks. Human society is characterized by the manifestation of swarm intelligence as a condition of joint organized activity, but the development of digital technologies brings swarm intelligence to a higher level. Creation of private swarms causes risks in the form of “Byzantine robot” – a hacked particle of the swarm, transmitting false information and jeopardizing the whole swarm. The solution is to incorporate elements of the economic game that support the values of honesty, cooperation and solidarity into the particle interactions. This leads to making privacy a “process” and changing its value status. Protecting privacy in swarm intelligence society has the potential to cause labor to lose its public status and acquire a private, hidden status and anonymize employment. Conclusion. The possible scenario of human society development towards the digital society of swarm intelligence can become a source of profound social changes and, above all, a rethinking of the social stratification based on the division of labor and social hierarchy in general.

Artificial Intelligence Based MPPT Techniques of Photo Voltaic System

In this paper, Particle Swarm Optimisation (PSO) investigates the global optimal solution by taking advantage of the memory of the particle and the swarm. PSO has evolved into one of the most significant Swam Intelligence techniques and Evolutionary Computation algorithms due to its characteristics of low constraint on the continuity of goal function and joint of search space, and capacity to adapt to dynamic environments. The development of algorithms over the years is then discussed, along with applications in multi-objective optimisation, neural networks, electronics, etc. The remaining issues and potential prospects for PSO research are then examined. One of the concepts of swarm intelligence introduced in the field of computing and artificial intelligence is particle swarm optimisation (PSO). PSO is a novel collective and distributed intelligent paradigm for problem solving, primarily in the field of optimisation, without centralized control or the provision of a global model. In this work, the basic PSO, its improvements, its applications to various systems, including electric power systems, and its premature convergence as well as its combination with other intelligent algorithms to enhance search capacity and shorten the time required to exit local optimums are all thoroughly reviewed.

IMPROVING BYZANTINE FAULT TOLERANCE IN SWARM ROBOTICS COLLECTIVE DECISION-MAKING SCENARIO VIA A NEW BLOCKCHAIN CONSENSUS ALGORITHM

Swarm robotics applies concepts of swarm intelligence to robotics. Discrete consensus achievement is one of the major behaviors found in swarm robotics. Various algorithms have been developed for discrete consensus achievement. However, existing discrete consensus achievement algorithms are vulnerable to Byzantine robots. Blockchain has been successfully used to mitigate the negative effect of Byzantine robots. Nevertheless, since the blockchain solution uses the Proof-of-Work blockchain consensus algorithm, it is vulnerable to the 51% attack. Besides, the swarm also takes longer to achieve consensus. This research proposes a novel blockchain consensus algorithm called Proof-of-Identity—which uses a private-public key pair and a swarm controller—to create a dynamically permissioned blockchain that would negate the 51%-attack problem associated with the Proof-of-Work algorithm while also reducing the consensus time. This proposed solution was tested against the classical solution and the existing blockchain solution using the collective perception scenario. Test results show that the Proof-of-Identity algorithm prevents the 51%-attack problem while improving the consensus time in comparison to the existing blockchain solution without affecting the exit probability.

Swarm Robotics and Intelligence

It's fascinating to observe how the concept of Swarm Intelligence in natural systems may be applied to human groupings, specifically in the realm of finance. According to the study you referenced, groups of financial traders were able to improve their accuracy in predicting weekly market indices by constructing online systems modeled after natural swarms. The increase in accuracy from 61% to 77% when predicting jointly as a real-times warm is a considerable improvement and demonstrates that the adoption of SI algorithms has the potential to considerably improve financial forecasting accuracy. It is also worth noting that the results' statistical significance (p=0.001) shows that the findings are reputable. Overall, the findings indicate that employing SI algorithms to allow groups of traders to construct real-time systems online could be a beneficial tool in the field of finance, potentially leading to more accurate financial predictions. More research is needed, however, to validate these findings and to investigate the possible applications and limitations of this approach.

A Minimal Drift MPPT Technique for Solar Grid-Tied Multilevel Converter by Multiobjective Swarm

A new multi-objective minimal-drift maximum power point tracking (MOMD-MPPT) technique is introduced to harness maximum power from single photovoltaic (PV) array fed multilevel converter (MLC). A drift in voltage arises with conventional MPPT techniques, which is minimized in this work with a new multi-objective solution. A new swarm optimization algorithm (NSOA) is used for MPPT, which combines the concept of swarm intelligence and pressure gradient force. It provides a minimal drift phenomenon for the solar MLC. Acceleration factor of the swarm particles in NSOA relies on pressure difference and distance. It leads to smoother convergence towards the operating point without trapping in the local minima. The presented algorithm is compared with well-known MPPT algorithms. Simulated results analyze the performance of MOMD-MPPT considering upper and lower voltage drift for PV fed MLC. Later, this algorithm is experimentally validated for a single PV array fed grid-tied multilevel converter. Recorded results show that the algorithm has minimal drift and improves the system performance in dynamic solar conditions.

Incentive-based cooperative traffic management: A state-of-the-art review

The DIT4TraM project capitalises on the concepts of swarm intelligence and mechanism design. One of its use-cases focuses on developing incentive-based traffic management approaches for local bottlenecks to nudge users towards the system-optimal behaviour, in the context of novel technological settings. This means that a traffic control problem has to be solved considering the technological setting, and that appropriate incentives for each driver need to be determined to ensure maximal fairness, compliance, and thus reachability of the desired state. To assess feasibility of such an approach, this paper reviews key literature in four areas: V2X communication technologies and architectures, data collection and state estimation for cooperative control, control methods for local, cooperative traffic management, and incentive schemes for traffic and mobility management.

Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications.

Known as an artificial intelligence subarea, Swarm Robotics is a developing study field investigating bio-inspired collaborative control approaches and integrates a huge collection of agents, reasonably plain robots, in a distributed and decentralized manner. It offers an inspiring essential platform for new researchers to be engaged and share new knowledge to examine their concepts in analytical and heuristic strategies. This paper introduces an overview of current activities in Swarm Robotics and examines the present literature in this area to establish to approach between a realistic swarm robotic system and real-world enforcements. First, we review several Swarm Intelligence concepts to define Swarm Robotics systems, reporting their essential qualities and features and contrast them to generic multi-robotic systems. Second, we report a review of the principal projects that allow realistic study of Swarm Robotics. We demonstrate knowledge regarding current hardware platforms and multi-robot simulators. Finally, the forthcoming promissory applications and the troubles to surpass with a view to achieving them have been described and analyzed.

The particle swarm optimization (PSO) algorithm application – A review

Particle Swarm Optimization (PSO) is one of the concepts of swarm intelligence inspired by studies in neurosciences, cognitive psychology, social ethology and behavioural sciences, introduced in the domain of computing and artificial intelligence as an innovative collective and distributed intelligent paradigm for solving problems, mostly in the domain of optimization, without centralized control or the provision of a global model. The PSO method has roots in genetic algorithms and evolution strategies and shares many similarities with evolutionary computing such as random generation of populations at system initialization or updating generations at optima search. This paper presents an extensive literature review on the concept of PSO, its application to different systems including electric power systems, modifications of the basic PSO to improve its premature convergence, and its combination with other intelligent algorithms to improve search capacity and reduce the time spent to come out of local optimums.

Distributed artificial bee colony approach for connected appliances in smart home energy management system

AbstractIn this study, we propose a computational intelligence model for the Internet of Things applications by applying the concept of swarm intelligence (SI) into connected devices. Particularly, decentralized management of smart home energy management system (HEMS) is taken into account in which connected appliances, by sharing information with each other, make the individual decisions for optimizing electricity prices of smart HEMS. Specifically, the study includes two main issues: (a) We propose a framework for decentralized management in smart HEMS; and (b) artificial bee colony (ABC) algorithm, a typical algorithm of SI techniques, has been applied for connected appliances in terms of communication and collaboration with each other to optimize the performance of the energy management system. Moreover, regarding the implementation, we develop and simulate a connected environment of smart home systems to evaluate the proposed approach. The simulation indicates the promising results in terms of optimizing the load balancing problem comparing with the conventional approach of the decentralized management system in smart home applications.

Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques

The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.

Swarm Intelligence: Establishing Behavioral Norms for the Emergence of Collective Leadership

To explicate the qualities of cooperation among leaders and their organizations during crisis, we studied the response to the 2013 Boston Marathon bombings. Through interviews and analysis, we discovered leaders successfully overcame obstacles that typically undermine collective crisis response. Qualitative analysis revealed five guiding behavioral principles that appeared to stimulate effective inter-agency leadership collaboration in high stakes. We draw upon concepts of collective leadership and swarm intelligence to interpret our observations and translate the findings into leader practices. We focus on replicable aspects of a meta- phenomenon, where collective action was greater than the sum of its parts; we do not evaluate individual leader behavior. Our findings provide a starting point for deeper exploration of how to bolster public safety by catalyzing enhanced inter-agency leadership behavior.

Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.

Time-varying algorithm for swarm robotics

Ever since the concept of swarm intelligence was brought out, a variety of control algorithms for swarm robotics has been put forward, and many of these algorithms are stable enough and efficient. Most of the researches only take an invariable controller which functions through the whole stage into consideration, the situation in which controller changes over time is rarely taken into account. However, there are limitations for invariable controller dominated algorithms in practical situation, which makes them unable to meet changing environment. On the contrary, variable controller is more flexible and can be able to adapt to complex environment. Considering such advantages, a time-varying algorithm for swarm robotics is presented in this paper. The algorithm takes time as one of the independent variables so that the controller is no longer fixed through the time, but can be changed over time, which brings more choices for the swarm robot system. In this paper, some relevant simulations are designed to test the algorithm. Different control strategies are applied on the same flock during the time, and a more complex, flexible and practical control effect is acquired successfully.

Remote sensing image classification for Jabalpur region using swarm classifiers

Swarm intelligence algorithms have been widely applied in solving many complex problems in different domains. In this paper, swarm intelligence-based cuckoo search (CS) and artificial bee colony optimisation (ABC) are used for satellite image classification of Jabalpur region (Madhya Pradesh), India. The reason for the selection of CS and ABC algorithm over other swarm intelligence concepts is the wide and efficient applicability of these concepts in different domains. The swarm classifiers-based obtained results are compared with other than swarm intelligence techniques mainly maximum likelihood classifier (MLC), minimum distance classifier (MDC) and fuzzy logic. Results are evaluated in terms of kappa coefficient, user's accuracy, producer's accuracy and overall accuracy. Results in terms of individual feature accuracy (user's accuracy and producer's accuracy) and consolidated accuracy (kappa coefficient and overall accuracy) indicates the dominance of swarm classifiers in comparison with other considered classifiers for the classification of land cover features.

An interactive agent-based framework for materialization-informed architectural design

Concepts of swarm intelligence are becoming increasingly relevant in the field of architectural design. An example is the use of agent-based modeling and simulation methods, which can help manage the complexity of building designs that feature many similar, but geometrically unique elements. Apart from leading to effective solutions and expanding the architectural design space, agent-based design methods can also be employed in integrated planning processes, in which the contributions of various disciplines take place in an integrated loop instead of being executed consecutively. We propose a computational framework for architectural design, in which agents represent building elements and/or joints between building elements. Behavior parameters, behavior weighting, and the environment can be modified in real-time while the agent system is running. Additionally, the designer can interact with individual agents directly, while slowing down or pausing agent movement if so desired. In the resulting design approach, the designer can globally adjust behavior parameters, while retaining local control over details where needed. To facilitate an integrative design process, domain-specific data and the results of external analysis can be included, either directly as input for agent behaviors, or by modifying the environment. We illustrate the potential of this computational framework using the example of the design of plate structures and show how this method can lead to quantifiable results while also attaining aesthetic goals. Furthermore, we provide an outlook toward possible further extensions of agent-based design methods in architecture.

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

Wireless Sensor Networks, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Congestion is a problem of importance in resource constrained Wireless Sensor Networks, especially for large networks, where the traffic loads exceed the available capacity of the resources . Sensor nodes are prone to failure and the misbehaviour of these faulty nodes creates further congestion. The resulting effect is a degradation in network performance, additional computation and increased energy consumption, which in turn decreases network lifetime. Hence, the data packet routing algorithm should consider congestion as one of the parameters, in addition to the role of the faulty nodes and not merely energy efficient protocols .Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN. Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, Performance, robustness and scalability. The main objective of this research paper is to propose congestion aware , energy efficient, routing approach that utilizes Ant Colony Optimization, in which faulty nodes are isolated by means of the concept of trust further we compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols, ACO Based Routing Protocol with Trust Based Congestion aware ACO Based Routing in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall TBC-ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.

Remote sensing image classification for Jabalpur region using swarm classifiers

Swarm intelligence algorithms have been widely applied in solving many complex problems in different domains. In this paper, swarm intelligence-based cuckoo search (CS) and artificial bee colony optimisation (ABC) are used for satellite image classification of Jabalpur region (Madhya Pradesh), India. The reason for the selection of CS and ABC algorithm over other swarm intelligence concepts is the wide and efficient applicability of these concepts in different domains. The swarm classifiers-based obtained results are compared with other than swarm intelligence techniques mainly maximum likelihood classifier (MLC), minimum distance classifier (MDC) and fuzzy logic. Results are evaluated in terms of kappa coefficient, user's accuracy, producer's accuracy and overall accuracy. Results in terms of individual feature accuracy (user's accuracy and producer's accuracy) and consolidated accuracy (kappa coefficient and overall accuracy) indicates the dominance of swarm classifiers in comparison with other considered classifiers for the classification of land cover features.

Drug Delivery Based on Swarm Microrobots

Advances in the development of technology have led to microrobots applications in medical fields. Drug delivery is one of these applications in which microrobots deliver a pharmaceutical compound to targeted cells. Chemotherapy and its side effects can then be minimized by this method. Two major constraints, however, must be considered: the robot’s onboard energy supply and the time needed for drug delivery. Furthermore, a microrobot must avoid biological restricted areas which we treat as obstacles in the path. The main objectives of this work were to find optimal paths to targeted cells and avoid collision with obstacles in the paths under a dynamic environment. In this study, we controlled motion of microrobots based on the concept of swarm intelligence. Artificial Bee Colony (ABC), the Best-so-far ABC, and the Particle Swarm Optimization (PSO) methods were employed to implement the collision detection and the boundary distance detection modules. Forces that drove or resisted blood flow as well as pressure in blood vessels were considered to approximate the effects of the environment on the microrobots. Numerical experiments were conducted using various obstacle environments. The results confirm that the proposed approaches were successful in avoiding obstacles and optimizing the energy consumption used to reach the target.

The Data Swarm

The traditional data warehouse model is no longer able to keep up with the evolution and changing requirements of the data analytic world. As we see the concept of a logical data warehouse gain momentum, there's a resulting need to drive a portion of the analytics closer to where the data is actually created and used. This paper uses the concept of swarm intelligence as a basis for simple, distributed analytics architecture to help address this need. It illustrates this with an example based on a chain of retail stores and demonstrates how this model could simplify the architecture and, at the same time, and increase data availability while decreasing cost.

Swarm Robotics: A Technological Advancement for Human-Swarm Interaction in Recent Era from Swarm-Intelligence Concept

Swarm Robotics: A Technological Advancement for Human-Swarm Interaction in Recent Era from Swarm-Intelligence Concept