Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Training Single Walled Carbon Nanotube based Materials to perform computation

This thesis illustrates the use of Single Walled Carbon Nanotube based materials for the solution of various computational problems by using the process of computer controlled evolution. The study aims to explore and identify three dimensions of a form of unconventional computing called, `Evolution-in-materio'. First, it focuses on identifying suitable materials for computation. Second, it explores suitable methods, i.e. optimisation and evolutionary algorithms to train these materials to perform computation. And third, it aims to identify suitable computational problems to test with these materials. Different carbon based materials, mainly single walled carbon nano-tubes with their varying concentrations in polymers have been studied to be trained for different computational problems using the principal of `evolution-in-materio'. The conductive property of the materials is used to train these materials to perform some meaningful computation. The training process is formulated as an optimisation problem with hardware in loop. It involves the application of an external stimuli (voltages) on the material which brings changes in its electrical properties. In order to train the material for a specific computational problem, a large number of configuration signals need to be tested to find the one that transforms the incident signal in such a way that a meaningful computation can be extracted from the material. An evolutionary algorithm is used to identify this configuration data and using a hardware platform, this data is transformed into incident signals. Depending on the computational problem, the specific voltages signals when applied at specific points on to the material, as identified by an evolutionary algorithm, can make the material behave as a Logic gate, a tone discriminator or a data classifier. The problem is implemented on two types of hardware platforms, one a more simple implementation using mbed ( a micro- controller) and other is a purpose-built platform for `Evolution-in-materio" called Mecobo. The results of this study showed that the single walled carbon nanotube composites can be trained to perform simple computational tasks (such as tone discriminator, AND, OR logic gates and a Half adder circuit), as well as complex computational problems such as Full Adder circuit and various binary and multiple class machine learning problems. The study has also identified the suitability of using evolutionary algorithms such as Particle Swarm Optimisation algorithm (PSO) and Differential evolution for finding solutions of complex computational problems such as complex logic gates and various machine learning classification problems. The implementation of classification problem with the carbon nanotube based materials also identified the role of a classifier. It has been found that K-nearest neighbour method and its variant kNN ball tree algorithm are more suitable to train carbon nanotube based materials for different classification problems. The study of varying concentrations of single walled carbon nanotubes in fixed polymer ratio for the solution of different computational problems provided an indication of the link between single walled carbon nanotubes concentration and ability to solve computational problem. The materials used in this study showed stability in the results for all the considered computational problems. These material systems can compliment the current electronic technology and can be used to create a new type of low energy and low cost electronic devices. This offers a promising new direction for evolutionary computation

Evolving robots: from simple behaviours to complete systems

Building robots is generally considered difficult, because the designer not only has to predict the interaction between the robot and the environment, but also has to deal with the ensuing problems. This thesis examines the use of the evolutionary approach in designing robots; the explorations range from evolving simple behaviours for real robots, to complex behaviours (also for real robots), and finally to complete robot systems — including controllers and body plans. A framework is presented for evolving robot control systems. It includes two components: a task independent Genetic Programming sub-system and a task dependent controller evaluation sub-system. The performance evaluation of each robot controller is done in a simulator to reduce the evaluation time, and then the evolved controllers are downloaded to a real robot for performance verification. In addition, a special rep¬ resentation is designed for the reactive robot controller. It is succinct and can capture the important characteristics of a reactive control system, so that the evolutionary system can efficiently evolve the controllers of the desired behaviours for the robots. The framework has been successfully used to evolve controllers for real robots to achieve a variety of simple tasks, such as obstacle avoidance, safe exploration and box-pushing. A methodology is then proposed to scale up the system to evolve controllers for more complicated tasks. It involves adopting the architecture of a behaviour-based system, and evolving separate behaviour controllers and arbitrators for coordination. This allows robot controllers for more complex skills to be constructed in an incremental manner. Therefore the whole control system becomes easy to evolve; moreover, the resulting control system can be explicitly distributed, understandable to the system designer, and easy to maintain. The methodology has been used to evolve control systems for more complex tasks with good results. Finally, the evolutionary mechanism of the framework described above is extended to include a Genetic Algorithm sub-system for the co-evolution of robot body plans — structuralparametersofphysicalrobotsencodedaslinearstringsofrealnumbers. An individual in the extended system thus consists of a brain(controller) and a body. Whenever the individual is evaluated, the controller is executed on the corresponding body for a period of time to measure the performance. In such a system the Genetic Programming part evolves the controller; and the Genetic Algorithm part, the robot body. The results show that the complete robot system can be evolved in this manner. i

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Real time locating system based on active RFID

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Telecomunicações). Universidade do Porto. Faculdade de Engenharia. 201

(Issue 3, July) Arena of AI@IITH

We are back again with 3 rd Issue of किरIITH - The Crowning Glory. This issue will take you to the tours of various researches, happenings & recognitions related to the latest trending research area - AI / ML @IITH and hence named as “#Arena of AI @IITH”. This special edition on AI initiatives at IITH was only possible because of the visionary thought process of our Director, Prof. B. S. Murty. Right from the cover page to Alumni column, we have ensured you get a flavour of AI in this issue. This issue contains rich research content for AI lovers. Read More..

Development of a smart grid for the proposed 33 KV ring main Distribution System in NIT Rourkela

The non-reliability of fossil fuels has forced the world to use energy efficiently. These days, it is being stressed to use the electrical power smartly so that energy does not go waste. And hence comes the concept of a Smart Grid. So it becomes necessary for reputed places of academics to develop the prototype of the same in their campus. National Institute of Technology (NIT) Rourkela intends to set up a 33KV Ring Main Distribution System including 33/0.433 KV substations in its campus. The present 11KV line will be discarded and replaced by the 33KV system. The main driving force behind this step by the management is to accommodate the stupendously increased power requirement of the institute. The above mentioned plan also includes, set up of Data Acquisition System (DAS) that intends to monitor the electrical equipment in the substations. This is being done not only to increase the accountability and reliability of the distribution system but also to encourage academic research in the distribution automation domain. All in all, an excellent step towards make the Grid, Smart. In this project work the focus is laid on getting load flow solution of the 33KV ring main system. Here the authors use a specialized algorithm for distribution network with high R/X value to obtain the load flow solution. Then using artificial neural networks computation, algorithms are implemented to do the load forecasting and dynamic tariff setting. At the end a Web Portal, the NITR e-Power Monitoring System is developed that will be an excellent interface to the public in general and will help the students of the institute to know their grid well. In short a conscious effort is put to make the grid more interactive