Calibração multi-objetivo do Tank Model utilizando diversos algoritmos genéticos e critérios de parada

Calibration of hydrologic models estimates parameter values that cannot be measured and enable the rainfall-runoff processes simulation. Multi-objective evolutionary algorithms can make the calibration faster and more efficient through an iterative process. However, the standard stopping criterion used to stop the iterative process is to reach a pre-defined number of iterations defined by the modeller. Alternatively, the Ticona stopping criterion is based on the minimum number of iterations required to achieve a determined number of non-dominated solutions in the Pareto front, resulting in a reduction of the computational time without losing performance during the calibration processes. We evaluated the Ticona stopping criterion in the Tank Model calibration. The calibration processes were performed using data from two river basins, with three genetic algorithms and two objective functions. The Ticona stopping criterion required a computational time 27.4% to 44.1% lower than using the standard stopping criterion and were obtaining similar results in simulated streamflow time series and similar values of the best set of parameters.A calibração de modelos hidrológicos estima os valores de parâmetros que não podem ser mensurados e permite a simulação dos processos chuva-vazão. Os algoritmos evolucionários multi-objetivos podem tornam a calibração mais rápida e eficiente por meio de processos iterativos. Contudo, o critério de parada padrão usado para encerrar o processo iterativo é baseado em um número de iterações pré-definido pelo usuário. Como alternativa, o critério de parada Ticona é baseado no número mínimo de iterações requerido para alcançar um determinado número de soluções não-dominadas na Frente de Pareto, resultando em um menor tempo computacional sem perda de desempenho durante a calibração. Neste estudo, foi avaliado o uso do critério de parada Ticona na calibração do Tank Model. A calibração foi realizada em duas bacias hidrográficas, usando três algoritmos genéticos e duas funções-objetivo. Os resultados indicaram um tempo computacional 27,4% a 44,1% menor quando utilizado o critério de parada Ticona em comparação com o critério de parada padrão, ao mesmo tempo que foram obtidos resultados similares quanto aos valores dos parâmetros calibrados e à série temporal de vazão simulada

CREATING SYNTHETIC ATTACKS WITH EVOLUTIONARY ALGORITHMS FOR INDUSTRIAL-CONTROL-SYSTEM SECURITY TESTING

Cybersecurity defenders can use honeypots (decoy systems) to capture and study adversarial activities. An issue with honeypots is obtaining enough data on rare attacks. To improve data collection, we created a tool that uses machine learning to generate plausible artificial attacks on two protocols, Hypertext Transfer Protocol (HTTP) and IEC 60870-5-104 (“IEC 104” for short, an industrial-control-system protocol). It uses evolutionary algorithms to create new variants of two cyberattacks: Log4j exploits (described in CVE-2021-44228 as severely critical) and the Industroyer2 malware (allegedly used in Russian attacks on Ukrainian power grids). Our synthetic attack generator (SAGO) effectively created synthetic attacks at success rates up to 70 and 40 percent for Log4j and IEC 104, respectively. We tested over 5,200 unique variations of Log4j exploits and 256 unique variations of the approach used by Industroyer2. Based on a power-grid honeypot’s response to these attacks, we identified changes to improve interactivity, which should entice intruders to mount more revealing attacks and aid defenders in hardening against new attack variants. This work provides a technique to proactively identify cybersecurity weaknesses in critical infrastructure and Department of Defense assets.Captain, United States Marine CorpsApproved for public release. Distribution is unlimited

Diversity assessment in many-objective optimization

Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multi-objective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for manyobjective optimization, which is an accumulation of the dissimilarity in the population, where an Lp-norm-based (p < 1) distance is adopted to measure the dissimilarity of solutions. Empirical results demonstrate our proposed metric can more accurately assess the diversity of solutions in various situations. We compare the diversity of the solutions obtained by four popular many-objective evolutionary algorithms using the proposed diversity metric on a large number of benchmark problems with two to ten objectives. The behaviors of different diversity maintenance methodologies in those algorithms are discussed in depth based on the experimental results. Finally, we show that the proposed diversity measure can also be employed for enhancing diversity maintenance or reference set generation in many-objective optimization

Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: Systematic literature review

Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies-this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives

Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment