Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

Extension of Max-Min Ant System with Exponential Pheromone Deposition Rule

The paper presents an exponential pheromone deposition approach to improve the performance of classical Ant System algorithm which employs uniform deposition rule. A simplified analysis using differential equations is carried out to study the stability of basic ant system dynamics with both exponential and constant deposition rules. A roadmap of connected cities, where the shortest path between two specified cities are to be found out, is taken as a platform to compare Max-Min Ant System model (an improved and popular model of Ant System algorithm) with exponential and constant deposition rules. Extensive simulations are performed to find the best parameter settings for non-uniform deposition approach and experiments with these parameter settings revealed that the above approach outstripped the traditional one by a large extent in terms of both solution quality and convergence time.Comment: 16th IEEE International Conference on Advanced Computing and Communication, 200

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

Comparative Analysis of Privacy Preservation Mechanism: Assessing Trustworthy Cloud Services with a Hybrid Framework and Swarm Intelligence

Cloud computing has emerged as a prominent field in modern computational technology, offering diverse services and resources. However, it has also raised pressing concerns regarding data privacy and the trustworthiness of cloud service providers. Previous works have grappled with these challenges, but many have fallen short in providing comprehensive solutions. In this context, this research proposes a novel framework designed to address the issues of maintaining data privacy and fostering trust in cloud computing services. The primary objective of this work is to develop a robust and integrated solution that safeguards sensitive data and enhances trust in cloud service providers. The proposed architecture encompasses a series of key components, including data collection and preprocessing with k-anonymity, trust generation using the Firefly Algorithm, Ant Colony Optimization for task scheduling and resource allocation, hybrid framework integration, and privacy-preserving computation. The scientific contribution of this work lies in the integration of multiple optimization techniques, such as the Firefly Algorithm and Ant Colony Optimization, to select reliable cloud service providers while considering trust factors and task/resource allocation. Furthermore, the proposed framework ensures data privacy through k-anonymity compliance, dynamic resource allocation, and privacy-preserving computation techniques such as differential privacy and homomorphic encryption. The outcomes of this research provide a comprehensive solution to the complex challenges of data privacy and trust in cloud computing services. By combining these techniques into a hybrid framework, this work contributes to the advancement of secure and effective cloud-based operations, offering a substantial step forward in addressing the critical issues faced by organizations and individuals in an increasingly interconnected digital landscape

Optimization of Airfield Parking and Fuel Asset Dispersal to Maximize Survivability and Mission Capability Level

While the US focus for the majority of the past two decades has been on combatting insurgency and promoting stability in Southwest Asia, strategic focus is beginning to shift toward concerns of conflict with a near-peer state. Such conflict brings with it the risk of ballistic missile attack on air bases. With 26 conflicts worldwide in the past 100 years including attacks on air bases, new doctrine and modeling capacity are needed to enable the Department of Defense to continue use of vulnerable bases during conflict involving ballistic missiles. Several models have been developed to date for Air Force strategic planning use, but these models have limited use on a tactical level or for civil engineer use. This thesis presents the development of a novel model capable of identifying base layout characteristics for aprons and fuel depots to maximize dispersal and minimize impact on sortie generation times during normal operations. This model is implemented using multi-objective genetic algorithms to identify solutions that provide optimal tradeoffs between competing objectives and is assessed using an application example. These capabilities are expected to assist military engineers in the layout of parking plans and fuel depots that ensure maximum resilience while providing minimal impact to the user while enabling continued sortie generation in a contested region

Survey analysis for optimization algorithms applied to electroencephalogram

This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques

Drone Swarms in Adversarial Environment

Drones are unmanned aerial vehicles (UAVs) operated remotely with the help of cameras, GPS, and on-device SD cards. These are used for many applications including civilian as well as military. On the other hand, drone swarms are a fleet of drones that work together to achieve a special goal through swarm intelligence approaches. These provide a lot of advantages such as better coverage, accuracy, increased safety, and improved flexibility when compared to a single drone. However, the deployment of such swarms in an adversarial environment poses significant challenges. This work provides an overview of the current state of research on drone swarms in adversarial environments including algorithms for swarming formation of robotic attack drones with their strengths and weaknesses as well as the attack strategies used by attackers. This work also outlines the common adversarial counter-attack methods to disrupt drone attacks consisting of detection and destruction of drone swarms along with their drawbacks, a counter UAV defense system, and splitting large-scale drones into unconnected clusters. After identifying several challenges, an optimized algorithm is proposed to split the large-scale drone swarms more efficiently

DETECTION AND IDENTIFICATION OF CYBERATTACKS IN CPS BY ‎APPLYING MACHINE LEARNING ALGORITHMS

بشكل عام ، تتكون الأنظمة السيبرانية الفيزيائية (المعروفة أيضًا باسم CPS) من مكونات متصلة بالشبكة تتيح الوصول عن بُعد والمراقبة والفحص. ونظرًا لأنه تم دمج هذه الانظمة في شبكة غير آمنة، قد تتعرض لهجمات إلكترونية متعددة. وفي حالة حدوث خرق لأمن الإنترنت، سيتمكن المخترق من إتلاف النظام ، مما قد يكون له آثار مدمرة. وبالتالي، من المهم للغاية الحفاظ على مصداقية الأنظمة السيبرانية الفيزيائية CPS. لقد أصبح من الصعب بشكل متزايد تحديد الاعتداءات على أنظمة (CPSs) حيث أصبحت هذه الأنظمة أكثر هدفًا للمتسللين والتهديدات الإلكترونية. من الممكن أن يجعل التعلم الآلي (ML) والذكاء الاصطناعي (AI) أيضًا الوضع أكثر أماناً,ويمكن أن تلعب التكنولوجيا القائمة على الذكاء الاصطناعي (AI) دورًا في نمو ونجاح مجموعة واسعة من أنواع المؤسسات المختلفة وبعدة طرق مختلفة. الهدف من هذا البحث وهذا النوع من تحليل البيانات هو تجنب اعتداءات CPS باستخدام تقنيات التعلم الآلي والذكاء الاصطناعي. تم تقديم إطارًا جديدًا لاكتشاف الهجمات الإلكترونية، والذي يستفيد من التعلم الآلي والذكاء الاصطناعي (ML). تبدأعملية تنظيف البيانات في قاعدة بيانات CPS بإجراء التطبيع للتخلص من الأخطاء والتكرارات ويتم ذلك بحيث تكون البيانات متسقة طوال الوقت. التحليل التمييزي الخطي هو الطريقة المستخدمة للحصول على الميزات ، وتعرف باسم (LDA). كآلية لتحديد الهجمات الإلكترونية، كانت العملية المستخدمة المقترحة هي عملية SFL-HMM بالتزامن مع إجراء HMS-ACO. تم تقييم الإستراتيجية الجديدة باستخدام محاكاة MATLAB، ومقارنة المقاييس التي تم الحصول عليها من تلك المحاكاة بالمقاييس الواردة من الطرق السابقة. لقد ثبت أن إطار عمل البحث أكثر فعالية بشكل كبير من التقنيات التقليدية في الحفاظ على درجات عالية من الخصوصية، كما قد اتضح من نتائج عدد من التحقيقات المنفصلة. بالإضافة إلى ذلك، من حيث معدل الاكتشاف، والمعدل الإيجابي الخاطئ، ووقت الحساب، على التوالي ، تتفوق الطريقة المقترحة في البحث على طرق الكشف التقليدية.In general, cyber-physical systems (also known as CPS) consist of networked components that allow for remote access, monitoring, and examination. Because they were integrated into an unsecured network, they have been the target of multiple cyberattacks. In the event that there was a breach in internet security, an adversary would be able to damage the system, which may have devastating effects. Thus, it is extremely important to maintain the credibility of the CPS. It is becoming increasingly difficult to identify assaults on computerised policing systems (CPSs) as these systems become more of a target for hackers and cyberthreats. It is feasible that Machine Learning (ML) as well as Artificial Intelligence (AI), may also make it the finest of times. Both of these outcomes are plausible. Technology based on artificial intelligence (AI) can play a role in the growth and success of a wide range of different types of enterprises in a variety of different ways. The goal of this type of data analysis is to avoid CPS assaults using machine learning and artificial intelligence techniques.   A new framework was offered for the detection of cyberattacks, which makes use of machine learning and artificial intelligence (ML). the process of cleaning up the data in the CPS database is starting by performing normalisation in order to get rid of errors and duplicates. This is done so that the data is consistent throughout. Linear Discriminant Analysis is the method that is used to get the features, and it is known as that (LDA). As a mechanism for the identification of cyberattacks, The suggested used process was the SFL-HMM process in conjunction with the HMS-ACO procedure. The new strategy is evaluated using a MATLAB simulation, and the metrics obtained from that simulation are compared to the metrics received from the earlier methods. The framework is shown to be substantially more effective than traditional techniques in the upkeep of high degrees of privacy, as demonstrated by the outcomes of a number of separate investigations. In addition, in terms of detection rate, false positive rate, and computation time, respectively, the framework beats traditional detection methods

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given