APPLICATION OF THE METAHEURISTIC TUNICATE SWARM ALGORITHM IN SOLVING APPLIED MECHANICS PROBLEMS

In this paper, the principles of a metaheuristic algorithm based on tunicate swarm behavior are shown. The Tunicate Swarm Algorithm (TSA for short) was used for solving problems in applied mechanics (speed reducer, cantilever beam and three-dimensional beam optimization). In the end, a comparison of results obtained by TSA and results obtained by other methods is given.

Special Issue on Bio-Logging and Robotics

In the studies done to date on the swarm behaviors of animals, many different observational techniques have been developed, indicating the importance of such detailed observations. The techniques of researchers aiming to capture the swarm behavior of animals, which is normally visually unobservable, have included attaching microsensors to honey bees or ants and data loggers (micro recorders) to birds or mammals. Such techniques, collectively known as “bio-logging,” can go far in clarifying why we feel animals that exhibit swarm behaviors seem to have a sort of collective intelligence, or “swarm intelligence.” Furthermore, studies on the swarm behaviors of animals may provide important clues to researchers in the field of swarm robotics. It is in this context that this special issue presents papers on bio-logging technologies, the collective behaviors of animals, and various advanced measurement technologies related to them. This special issue consists of one review article and 14 research papers. The subjects cover a wide range of areas, including control engineering, data science, and ecology. Thus, bio-logging is an interdisciplinary area that can expect to see much growth in the near future. The editors are confident that this issue will greatly contribute to further progress in the field of bio-logging.

A cellular platform for the development of synthetic living machines

Robot swarms have, to date, been constructed from artificial materials. Motile biological constructs have been created from muscle cells grown on precisely shaped scaffolds. However, the exploitation of emergent self-organization and functional plasticity into a self-directed living machine has remained a major challenge. We report here a method for generation of in vitro biological robots from frog (Xenopus laevis) cells. These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects. The biological robots arise by cellular self-organization and do not require scaffolds or microprinting; the amphibian cells are highly amenable to surgical, genetic, chemical, and optical stimulation during the self-assembly process. We show that the xenobots can navigate aqueous environments in diverse ways, heal after damage, and show emergent group behaviors. We constructed a computational model to predict useful collective behaviors that can be elicited from a xenobot swarm. In addition, we provide proof of principle for a writable molecular memory using a photoconvertible protein that can record exposure to a specific wavelength of light. Together, these results introduce a platform that can be used to study many aspects of self-assembly, swarm behavior, and synthetic bioengineering, as well as provide versatile, soft-body living machines for numerous practical applications in biomedicine and the environment.

Artistic Perception of Leo Tolstoy’s Conception in V. P. Aksyonov’s Trilogy Generations of Winter: On the Question of the Individual in History

This article investigates V. P. Aksyonov’s argument with L. N. Tolstoy about the role of personality in history in his trilogy Generations of Winter. The saga protagonists’ spiritual awakening and self-identification are discussed in the context of Tolstoy’s view on the activity of the general mass of people who take part in a historical event and determine its outcome. This allows to elicit Aksyonov’s view on man’s personal responsibility under Stalinism. The main sources of this paper are Tolstoy’s War and Peace and Aksyonov’s Generations of Winter. Its purpose is to explain Aksyonov’s artistic conceptualization of the role of the masses and the individual under the Stalinist regime and in the Great Patriotic War. The major results of the research have been achieved by comparing the artistic devices employed by Tolstoy in fracturing the myth of Napoleon’s genius as leader of the general mass with Aksyonov’s approach in satirical portrayal of Stalin. The article explores Aksyonov’s contribution to the literary tradition of the grotesque, his utilization of the functions of the body “bottom” and of Russian popular demonic motifs in the depiction of Stalin. The paper’s author utilizes a comparative hermeneutical analysis and concludes that Aksyonov’s metaphor of “Stalinist hypnosis” is the writerly equivalent of Tolstoy’s conception of “swarm behavior”, and that Aksyonov’s argument with Tolstoy’s negation of the role of the individual in history is a postmodern playful ironic device, a means for his conceptualization and revision of Leo Tolstoy’s philosophic-historical view for the twentieth century.

Development of swarm behavior in artificial learning agents that adapt to different foraging environments

Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artificial learning agent that interacts with its neighbors and surroundings in order to make decisions and learn from them. Within a reinforcement learning framework, we discuss one-dimensional learning scenarios where agents need to get to food resources to be rewarded. We observe how different types of collective motion emerge depending on the distance the agents need to travel to reach the resources. For instance, strongly aligned swarms emerge when the food source is placed far away from the region where agents are situated initially. In addition, we study the properties of the individual trajectories that occur within the different types of emergent collective dynamics. Agents trained to find distant resources exhibit individual trajectories that are in most cases best fit by composite correlated random walks with features that resemble Lévy walks. This composite motion emerges from the collective behavior developed under the specific foraging selection pressures. On the other hand, agents trained to reach nearby resources predominantly exhibit Brownian trajectories.

Swarms of Aquatic Unmanned Surface Vehicles (USV), a Review From Simulation to Field Implementation

Swarm robotic is a field of multi-robotics in which the robot’s behavior is inspired from nature. With rapid development in the field of the multi-robotics and the lack of efficacy in traditional centralized controls method, decentralized nature inspired swarm algorithms were introduced to control the swarm behavior. Unmanned surface vehicles (USVs) are marine crafts that they can operate autonomously. Due to their potential in operating in different areas, these vehicles have been used for variety of reason including patrolling, border protection, environmental monitoring and oil spill confrontation. This paper provides a review of the Swarm of USVs, their application, simulation environments and the algorithms that has been used in the past and current projects.