Artificial Swarm Research Articles

Swarming sensations: robo-bees and the politics of the swarm in Black Mirror

ABSTRACT This article investigates how robotic swarming entails new modes of sensing that not only blur the lines between human, insect and machine – and hence between natural and prosthetic sensoria – but also transcend beyond what the human sensory apparatus can possibly process and comprehend. Based on examples and analytical insights from the popular Netflix series Black Mirror the article analyzes how robotic surrogate bees capable of pollinating the world’s flora in order to prevent an ecological catastrophe is depicted in the episode “Hated in the Nation”. Thus, the article not only demonstrates how artificial intelligence and robotic swarms can expand, refine, or disrupt the sensory capacity of human perception – it also shows how the relationship between natural and artificial swarming is troubled by historical, social, and technical ties that altogether shed new light on the idea of machine autonomy.

OPTIMIZATION ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A REVIEW

With the growth and development of the Internet, the devices and the hosts connected to the Internet have become the target for attackers and intruders. Consequently, the integrity of systems and data has become more sophisticated. Meanwhile, many institutions suffer from money-losing or other losses due to attacks on computer systems. Accordingly, the detection of intrusion and attacks has become a challenge and a vital necessity at the same time. Many different methods were used to build intrusion detection systems (IDSs), and all these methods seek to a plus the efficiency of intrusion detection systems. This paper is a survey which tries to covers some of the optimization algorithms used in the field of intrusion detection in past ten years such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Cuttlefish Algorithms (CFA), and Particle Swarm Optimization (PSO). It is hoped that this review will provide useful insights about the intrusion detection literature and is a good source for anyone interested in applying one of the used optimization algorithms in the field of intrusion detection.

Interpretable Artificial Swarm Intelligence in Complex Neurosurgical Decision Making: Part 1

Interpretable Artificial Swarm Intelligence in Complex Neurosurgical Decision Making: Part 1

Multi-objective configuration optimization method for a diesel-based hybrid energy system

In this paper, a configuration optimization method for a diesel hybrid energy system has been proposed. The method is based on a fuzzy artificial bee colony optimization mechanism that considers the power supply characteristics of an isolated hybrid energy system. First, a mathematical model based on a diesel hybrid energy system and a configuration optimization objective function that considers cost and load shortages is constructed. Then, an objective function is optimized in combination with fuzzy rules and artificial swarming algorithms to obtain a system configuration that meets multiple objective requirements. Finally, the system configurations of three hybrid modes of photovoltaic (PV)/energy storage/diesel, wind power/energy storage/diesel, and PV/wind power/energy storage/diesel are compared, and the pollutant emissions of the different systems are analyzed. Experimental results show that the new method exhibits good configuration optimization and performance, and it is a practical approach for solving the problem of microgrid system configuration.

Measuring Group Personality with Swarm AI

The aggregation of individual personality assessments to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive realworld team performance. Artificial Swarm Intelligence (ASI)—a technology that enables networked teams to think together in real-time and answer questions as a unified system—promises a solution to these limitations by enabling teams to collectively complete a personality assessment, whereby the team uses ASI to converge upon answers that best represent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) assessment both as individuals, and as a realtime system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional aggregation methods, showing at least a 91.8% increase in average correlation with the measured outcome variables, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches.

Modeling and Anlysis for Diagnosis Skin Lesions using Modern Artificial Swarm Intelligence Techniques (MASITs)

MASITs provides an optimum outcomes if it is not probable to become the solutions of huge inflexible optimization difficulties. Computerized investigation of skin lesions is a significant problem in data retrieval for medical imaging, it supports human experts to enhance their choice construction for rapid and accurate analysis of unhealthy nevi and other skin diseases. In this article, computerized investigation of skin lesions has been addressed, by an adjustment of controlling swarm intelligence system (Artifical Bee Colony{ABC}).The modified system is hybridized with a search technique for improved performance. Experimental outcomes on a level of medical images of early diagnosis skin lesions confirmation that this technique outclasses conventional mathematical approaches for the cases in the standard. It is identical good and regularly higher to advanced systems in the area in relationships of mathematical accuracy. The chief benefit of the proposed technique is that this diagnosis can segment skin lesions by resolve images. So, additional comprehensive features can be found from the segmented portion of the lesion, which in turn contributes on organization medical service accuracy.

New Polymorphs of 2D Indium Selenide with Enhanced Electronic Properties.

The 2D semiconductor indium selenide (InSe) has attracted significant interest due its unique electronic band structure, high electron mobility, and wide tunability of its band gap energy achieved by varying the layer thickness. All these features make 2D InSe a potential candidate for advanced electronic and optoelectronic applications. Here, the discovery of new polymorphs of InSe with enhanced electronic properties is reported. Using a global structure search that combines artificial swarm intelligence with first-principles energetic calculations, polymorphs that consist of a centrosymmetric monolayer belonging to the point group D 3d are identified, distinct from well-known polymorphs based on the D 3h monolayers that lack inversion symmetry. The new polymorphs are thermodynamically and kinetically stable, and exhibit a wider optical spectral response and larger electron mobilities compared to the known polymorphs. Opportunities to synthesize these newly discovered polymorphs and viable routes to identify them by X-ray diffraction, Raman spectroscopy, and second harmonic generation experiments are discussed.

Optimizing the Cargo Location Assignment of Retail E-Commerce Based on an Artificial Fish Swarm Algorithm

An efficient storage strategy for retail e-commerce warehousing is important for minimizing the order retrieval time to improve the warehouse-output efficiency. In this paper, we consider a model and algorithm to solve the cargo location problem in a retail e-commerce warehouse. The problem is abstracted into storing cargo on three-dimensional shelves, and the mathematical model is built considering three objectives: efficiency, stability, and classification. An artificial swarm algorithm is designed to solve the proposed models. Computational experiments performed on a warehouse show that the proposed approach is effective at solving the cargo location assignment problem and is significant for the operation and organization of a retail e-commerce warehouse.

Retraction notice to “Kinetics and thermodynamics of methyl orange adsorption from aqueous solutions—artificial neural network-particle swarm optimization modeling” [J. Mol. Liq, 218, June 2016 354–362

Retraction notice to “Kinetics and thermodynamics of methyl orange adsorption from aqueous solutions—artificial neural network-particle swarm optimization modeling” [J. Mol. Liq, 218, June 2016 354–362

Toward target search approach of swarm robotics in limited communication environment based on robot chains with elimination mechanism

Swarm robotics refers to artificial swarm systems composed of a large number of autonomous mobile robots with relatively simple structures and functions. One of the basic problems of swarm robotics involves the target search process, which entails a cooperative search using limited perception and local interaction of robots under a self-organizing mechanism. When communication is limited, the connectivity of swarm robotics may decrease, leading to the failure of the target search task. This article describes a new target search method based on the robot chain model and the elimination mechanism. The proposed method allows the target search task to be completed efficiently and reliably while maintaining the connectivity of the robots. Experimental results show that the proposed algorithm offers better performance than conventional techniques in terms of search speed and success rate, and provides an effective method for solving the target search problem using swarm robotics in limited-communication environments.

Web Inspection Algorithm for Low Contrast Paper Defects Based on Artificial Bee Colony Optimization

Traditional paper defect detection algorithms have the problems of low detection rate and poor anti-interference ability for low contrast paper defects such as cracks and folds. Considering these problems, an algorithm of low contrast paper defects based on artificial bee colony optimization was presented. Firstly, the Gabor filter was used to eliminate the texture elements and enhance the contrast. Then, the optimal segmentation threshold of 2-D OSTU was obtained by taking the trace of the dispersion matrix of the filtered paper disease image as the objective function of the artificial swarm optimization. Finally, according to the best segmentation threshold, the paper image was detected by 2-D OSTU method. The simulation results indicated that this algorithm has the advantages of high detection rate, accurate positioning and good anti-disturbance performance for low contrast paper defects.

Udwadia–Kalaba constraint-based tracking control for artificial swarm mechanical systems: dynamic approach

A novel swarm tracking control for artificial swarm mechanical systems consisting of multiple mechanical agents is proposed. In this paper, the agents could not only perform some biological swarm behaviors, such as the repulsion and attraction between agents, but also track the moving target or desired trajectory together. Based on the artificial potential functions, the kinematic modeling of each agent is constructed. The kinematic performance of the swarm system is analyzed, which includes convergence, tracking, aggregation and formation. Inspired by Udwadia–Kalaba constraints, the kinematic modeling of the swarm system is treated as servo constraints and formulated in the second-order form. With the second-order constraints, the explicit servo constraint forces are derived. In virtue of the constraint forces in the closed form, we creatively design a dynamic control for each agent which guarantees the controlled swarm system to obey the required motion. The proposed control scheme is proved by a series of theorems and illustrated by the simulation of multiple nonholonomic mobile robots.

Performance comparison of population-based optimization algorithms for air traffic control

Purpose The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem. Design/methodology/approach Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used. Findings Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results. Research limitations/implications In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations. Practical implications The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications. Social implications By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations. Originality/value The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.

Flocking in complex environments-Attention trade-offs in collective information processing.

The ability of biological and artificial collectives to outperform solitary individuals in a wide variety of tasks depends crucially on the efficient processing of social and environmental information at the level of the collective. Here, we model collective behavior in complex environments with many potentially distracting cues. Counter-intuitively, large-scale coordination in such environments can be maximized by strongly limiting the cognitive capacity of individuals, where due to self-organized dynamics the collective self-isolates from disrupting information. We observe a fundamental trade-off between coordination and collective responsiveness to environmental cues. Our results offer important insights into possible evolutionary trade-offs in collective behavior in biology and suggests novel principles for design of artificial swarms exploiting attentional bottlenecks.

Solving forest harvesting problem with artificial intelligence approaches: An example in Taiwan

In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives.

Discrimination between Healthy and Unhealthy Mole Lesions using Artificial Swarm Intelligence

In recent years, occurrence rates of skin melanoma have shown a rapid increase, resulting in enhancements to death rates. Based on the difficulty and subjectivity of human clarification, computer examination of dermoscopy images has thus developed into a significant research field in this area. One the reasons for applying heuristic methods is that good solutions can be developed with only reasonable computational exertion. This paper thus presents an artificial swarm intelligence method with variations and suggestions. The proposed artificial bee colony (ABC) is a more suitable algorithm in comparison to other algorithms for detecting melanoma in the skin tumour lesions, being flexible, fast, and simple, and requiring fewer adjustments. These is characteristics are recognized assisting dermatologists to detect malignant melanoma (MM) at the lowest time and effort cost. Automatic classification of skin cancers by using segmenting the lesion’s regions and selecting of the ABC technique for the values of the characteristic principles allows. Information to be fed into several well-known algorithms to obtain skin cancer categorization: in terms of whether the lesion is suspicious, malignant, benign (healthy and unhealthy nevi). This segmentation approach can further be utilized to develop handling and preventive approaches, thus decreasing the danger of skin cancer lesions. One of the most significant stages in dermoscopy image examination is the segmentation of the melanoma. Here, various PH2 dataset image were utilized along with their masks to estimate the accuracy, sensitivity, and specificity of various segmentation techniques. The results show that a modified automatic based on ABC images have the highest accuracy and specificity compares with the other algorithms. The results show that a modified automatic based on ABC images displayed the highest accuracy and specificity in such testing.

Probabilistic consensus decision making algorithm for artificial swarm of primitive robots

This paper presents a consensus algorithm for artificial swarms of primitive agents, such as robots with limited sensing, processing, and communication capabilities. The presented consensus algorithm provides solutions of collective decision making for a connected network of robots. The decisions are considered abstract choices without difference, thus the algorithm can be “programmed” for a broad range of applications with specific decisions. Each robot in the swarm is considered a probabilistic finite state machine, whose preferences towards a set of discrete states are defined as a probabilistic mass function. Then, the individual preferences are updated via local negotiation with directly connected robots, followed by a convergence improvement process. The presented algorithm is evaluated for the effects of network topology and scalability (i.e., the number of decisions and the size of the swarm) on convergence performance.

Stable emergent formations for a swarm of autonomous car-like vehicles

A biological swarm is an ideal multi-agent system that collectively self-organizes into bounded, if not stable, formations. A mathematical model, developed appropriately from some principle of swarming, should enable one, therefore, to study formation strategies for multiple autonomous robots. In this article, based on the hypothesis that swarming is an interplay between long-range attraction and short-range repulsion between the individuals in the swarm, a planar individual-based or Lagrangian swarm model is constructed using the Direct Method of Lyapunov. While attraction ensures the swarm is cohesive, meaning that the individuals in the swarm remain close to each other at all times, repulsion ensures that the swarm is well-spaced, meaning that no two individuals in the swarm occupy the same space at the same time. Via a novel Lyapunov-like function with attractive and repulsive components, the article establishes the global existence, uniqueness, and boundedness of solutions about the centroid. This paves the way to prove that the swarm model, governed by a system of first-order ordinary differential equations (ODEs), is cohesive and well-spaced. The article goes on to show that the artificial swarm can collectively self-organize into two stable formations: (i) a constant arrangement about the centroid when the system has equilibrium points, and (ii) a highly parallel formation when the system does not have equilibrium points. Computer simulations not only illustrate these but also reveal other emergent patterns such as swirling structures and random-like walks. As an application, we tailor the model accordingly and propose new autonomous steering laws giving rise to pattern-forming for multiple nonholonomic car-like vehicles.

The lead recovery prediction from lead concentrate by an artificial neural network and particle swarm optimization

ABSTRACTPrediction of lead recovery during the leaching process is required to increase the process efficiency by proper modeling. In this study, a new artificial neural network predictive model based on the particle swarm optimization (ANN-PSO) was developed to predict the lead recovery by a hydrometallurgical method of lead concentrate leaching using fluoroboric acid. A multi-layer ANN-PSO model was trained for developing a predictive model based on the main effective parameters on the lead leaching process. The input parameters of the ANN-PSO model were leaching time, liquid/solid ratio, stirring speed, temperature and fluoroboric acid concentration, while the lead recovery during leaching was the output. The results indicate that the proposed ANN-PSO model can be effectively predicted the lead recovery during lead concentrate leaching using fluoroboric acid.

Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making

This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.