Multi-objective Genetic Search Research Articles

A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.

Network-level Design Space Exploration of Resource-constrained Networks-of-Systems

Driven by recent advances in networking and computing technologies, distributed application scenarios are increasingly deployed on resource-constrained processing platforms. This includes networked embedded and cyber-physical systems as well as edge computing in mobile applications and the Internet of Things (IoT). In such resource-constrained Networks-of-Systems (NoS), computation and communication workloads need to be carefully co-optimized yet are tightly coupled. How to optimally partition and schedule application tasks among an appropriately designed NoS architecture requires a simultaneous consideration of design parameters from applications and processing platforms all the way to network configurations. Traditionally, however, systems and networks are designed in isolation and combined in an ad hoc manner, which ignores joint effects and optimization opportunities. To systematically explore and optimize NoS design spaces, a higher level of design abstraction on top of traditional system and network design is required. In this article, we propose a novel network-level design methodology for resource-constrained NoS optimization and design space exploration. A key component in such a design flow is fast yet accurate network/system co-simulation to rapidly evaluate NoS parameters with high fidelity. We first introduce a novel NoS simulator (NoSSim) that integrates source-level simulation models of applications with a host-compiled system simulation platform and a reconfigurable network simulation backplane to accurately capture system and network interactions. The co-simulation platform is further combined with model generation tools and a multi-objective genetic search algorithm to provide a comprehensive and fully automated NoS design space exploration framework. Finally, we apply our network-level design flow on several state-of-art IoT/mobile design case studies. Results show that NoSSim can achieve more than 86% simulation accuracy on average as compared to a real-world edge device cluster, where sensitivities to various design parameters are faithfully captured with high fidelity. When applying our network-level design space exploration methodology, design decisions are automatically optimized, where non-obvious NoS configurations are discovered outperforming manually designed solutions by more than 45%.

Improving Efficiency of Graph Clustering by Genetic Algorithm Using Multi-Objective Optimization

ABSTRACT Information networks, like social networks on the internet, evolve naturally without constraint on degree or connectivity. The distribution of degree of such network is exponential, with a few nodes having a large degree, forming what is called a scale-free network. Scale-free networks form communities, or clusters of nodes. Discovering such clusters is the most important step for analysis of the information network. This is a NP-hard problem. Recently, genetic algorithm based works are reported, where the optimum number of clusters evolve automatically. The optimization criterion, the modularity index, is used as the fitness function of the chromosome. We observed that, if the diameter of the clusters are constrained to a lower value, during evolution of genetic search, a faster convergence is achieved. We used a multi-objective genetic search with two optimization criteria: the modularity index (the higher the better), and the largest diameter (the lower the better) of the communities defined by a chromosome. Simulation results, using network with known communities, show that using this multi-objective GA the result is stable, and achieves better modularity compared to the most often used Louvain algorithm.

Multiobjective optimization–based fault‐tolerant flight control system design

SummaryThe loss of measurements used for controller scheduling or envelope protection in modern flight control systems due to sensor failures leads to a challenging fault‐tolerant control law design problem. In this article, an approach to design such a robust fault‐tolerant control system, including full envelope protections using multiobjective optimization techniques, is proposed. The generic controller design and controller verification problems are derived and solved using novel multiobjective hybrid genetic optimization algorithms. These algorithms combine the multiobjective genetic search strategy with local, single‐objective optimization to improve convergence speed. The proposed strategies are applied to the design of a fault‐tolerant flight control system for a modern civil aircraft. The results of an industrial controller verification and validation campaign using an industrial benchmark simulator are reported.

Reflection Symmetry-Integrated Image Segmentation

This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.

Tree network channels as fluid distributors constructing double-staircase polymer electrolyte fuel cells

In this article, constructal tree-like channel networks are introduced as a fuel cell fluid distribution concept, which also optimizes the shape of polymer electrolyte fuel cells. This concept is the main contribution of the article. To perform quantitative calculations based on this concept, a one-dimensional model, accounting for oxygen consumption in the feed channel, oxygen mass transfer between the channel and the backing layer, and oxygen mass transfer through the backing layer to the catalyst layer, is used to predict the polarization curve of a so-constructed polymer electrolyte fuel cell. Pressure drop and pumping power required for the fluid circulation is estimated. Multiobjective genetic search is performed to maximize the net power density with respect to constructal parameters and operating conditions, leading to the optimized tree network. It is found that the use of tree networks instead of the traditional, nonbifurcating serpentine channels in rectangular systems can provide substantially improved cell performance due their intrinsic advantage with respect to both mass transfer and pressure drop. The resulting “double-staircase” shape of the fuel cells differs from the traditional rectangular shape while maintaining simplicity, and it is determined based on the functionality of the flow distribution system.