A Brief Review of Bio-Inspired Algorithms in Computational Perspective - Bio Inspired Algorithms

Computing over the years has evolved from being simplex mathematical processing machine to more sophisticated problem solving entity pushing limits around reasoning and intelligence. Along the way, lots scientists and engineers have closely observed some of the biological processes achieving certain things in a more efficient and simple fashion than traditional computational mechanisms. This has led to development of various techniques and algorithms which try and mimic these biological processes and are categorised under, Bio-Inspired Computing

Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network

Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview

Biology has often been used as a source of inspiration in computer science and engineering. Bioinspired principles have found their way into network node design and research due to the appealing analogies between biological systems and large networks of small sensors. This paper provides an overview of bioinspired principles and methods such as swarm intelligence, natural time synchronization, artificial immune system and intercellular information exchange applicable for sensor network design. Bioinspired principles and methods are discussed in the context of routing, clustering, time synchronization, optimal node deployment, localization and security and privacy

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm

Adapting Artificial Immune Algorithms For University Timetabling

Penjadualan kelas dan peperiksaan di universiti adalah masalah pengoptimuman berkekangan tinggi. University class and examination timetabling are highly constrained optimization problems

BIDS: Bio-Inspired, Collaborative Intrusion Detection for Software Defined Networks

With network attacks becoming more sophisticated and unpredictable, detecting their onset and mitigating their effects in an automated manner become increasingly challenging. Lightweight and agile detection mechanisms that are able to detect zero-day attacks are in great need. High true-negative rate and low false-positive rate are the most important indicators for a intrusion detection system. In this paper, we exploit the logically-centralised view of Software-Defined Networking (SDN) to increase true-negative rate and lower false-positive rate in a intrusion detection system based on the Artificial Immune System (AIS). We propose the use of an antibody fuser in the controller to merge and fuse the mature antibody sets trained in the individual switches and turn the real intrusion records each switch has seen into antibodies. Our results show that both the false-positive rate and true-negative rate experience significant improvement with the number of local antibody sets fused grows, consuming less cpu usage overhead. A peak improvement can reach over 80% when antibody sets from all switches are taken into consideration