Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

Constrained Navigation with Mandatory Waypoints in Uncertain Environment

Also available online at http://www.ijisce.org/admin/upload/946980IJISCE-Constrained%20Navigation%20with%20Mandatory%20Waypoints%20in%20Uncertain%20Environment.pdfInternational audienceThis paper presents a hybrid solving method for vehicle path planning problems. As part of the vehicle system architecture (vetronic), planning is dynamic and has to be activated on-line, which requires response times to be compatible with mission execution. The proposed approach combines constraint solving techniques with an Ant Colony Optimization (ACO). The hybridization relies on a static probing technique which builds up a search strategy using a distance information between problem variables and a heuristic solution. Various forms of this approach are compared and evaluated on real world scenarios. Preliminary results exhibit response times close to vehicle control requirements, on realistic problem instances

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods

New Anomaly Network Intrusion Detection System in Cloud Environment Based on Optimized Back Propagation Neural Network Using Improved Genetic Algorithm

Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over an open environment (Internet), this lead to different matters related to the security and privacy in cloud computing. Thus, defending network accessible Cloud resources and services from various threats and attacks is of great concern. To address this issue, it is essential to create an efficient and effective Network Intrusion System (NIDS) to detect both outsider and insider intruders with high detection precision in the cloud environment. NIDS has become popular as an important component of the network security infrastructure, which detects malicious activities by monitoring network traffic. In this work, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using an Improved Genetic Algorithm (IGA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Since,  Learning rate and Momentum term are among the most relevant parameters that impact the performance of BPNN classifier, we have employed IGA to find the optimal or near-optimal values of these two parameters which ensure high detection rate, high accuracy and low false alarm rate. The CloudSim simulator 4.0 and DARPA’s KDD cup datasets 1999 are used for simulation. From the detailed performance analysis, it is clear that the proposed system called “ANIDS BPNN-IGA” (Anomaly NIDS based on BPNN and IGA) outperforms several state-of-art methods and it is more suitable for network anomaly detection

How to Use Metaheuristics for Design of Symmetric-Key Primitives

The ultimate goal of designing a symmetric-key cryptographic primitive often can be formulated as an optimization problem. So far, these problems mainly have been solved with trivial algorithms such as brute force or random search. We show that a more advanced and equally versatile class of search algorithms, called metaheuristics, can help to tackle optimization problems related to design of symmetric-key primitives. We use two nature-inspired metaheuristics, simulated annealing and genetic algorithm, to optimize in terms of security the components of two recent cryptographic designs, SKINNY and AES-round based constructions. The positive outputs of the optimization suggest that metaheuristics are non-trivial tools, well suited for automatic design of primitives