"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'

In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

新たな進化的及びニューロン計算による分類問題に関する研究

富山大学・富理工博甲第172号・銭孝孝・2020/3/24富山大学202

Smart Distribution Power Systems Reconfiguration using a Novel Multi-agent Approach

The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 25665535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of trade-off solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events, based on only the detector\u27s own data structure and the ID domain truth set, and respond heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate

Efficient Learning Machines

Computer scienc

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities