Covariance Matrix Adaptation Research Articles

Joint contrastive learning and belief rule base for named entity recognition in cybersecurity

Named Entity Recognition (NER) in cybersecurity is crucial for mining information during cybersecurity incidents. Current methods rely on pre-trained models for rich semantic text embeddings, but the challenge of anisotropy may affect subsequent encoding quality. Additionally, existing models may struggle with noise detection. To address these issues, we propose JCLB, a novel model that Joins Contrastive Learning and Belief rule base for NER in cybersecurity. JCLB utilizes contrastive learning to enhance similarity in the vector space between token sequence representations of entities in the same category. A Belief Rule Base (BRB) is developed using regexes to ensure accurate entity identification, particularly for fixed-format phrases lacking semantics. Moreover, a Distributed Constraint Covariance Matrix Adaptation Evolution Strategy (D-CMA-ES) algorithm is introduced for BRB parameter optimization. Experimental results demonstrate that JCLB, with the D-CMA-ES algorithm, significantly improves NER accuracy in cybersecurity.

Automated placement of analog integrated circuits using priority-based constructive heuristic

This paper presents a heuristic approach for solving the placement of Analog and Mixed-Signal Integrated Circuits. Placement is a crucial step in the physical design of integrated circuits. During this step, designers choose the position and variant of each circuit device. We focus on the specific class of analog placement, which requires so-called pockets, their possible merging, and parametrizable minimum distances between devices, which are features mostly omitted in recent research and literature. We formulate the problem using Integer Linear Programming and propose a priority-based constructive heuristic inspired by algorithms for the Facility Layout Problem. Our solution minimizes the perimeter of the circuit’s bounding box and the approximated wire length. Multiple variants of the devices with different dimensions are considered. Furthermore, we model constraints crucial for the placement problem, such as symmetry groups and blockage areas. Our outlined improvements make the heuristic suitable to handle complex rules of placement. With a search guided either by a Genetic Algorithm or a Covariance Matrix Adaptation Evolution Strategy, we show the quality of the proposed method on both synthetically generated and real-life industrial instances accompanied by manually created designs. Furthermore, we apply reinforcement learning to control the hyper-parameters of the genetic algorithm. Synthetic instances with more than 200 devices demonstrate that our method can tackle problems more complex than typical industry examples. We also compare our method with results achieved by contemporary state-of-the-art methods on the MCNC and GSRC datasets.

Multi-Source energy optimization method for supersonic aircraft based on multi-objective adaptive covariance matrix and chaotic search group algorithm

In the trend of increasing aircraft electrical power levels, future supersonic aircraft will have an urgent need for multi-source, adaptive, and high-energy-efficient approach to electrical power generation. In this paper, a multi-source energy optimization method based on multi-objective optimization algorithm MOACCGA is proposed. The method considers fuel consumption rate and thrust as the objective functions, with engine safety as a constraint. Based on the establishment of a small bypass ratio turbofan engine with multi-source energy extraction system, the influence mechanism of energy extraction on engine performance is analyzed. It is concluded that the generation of multi-source energy is optimizable, and the objective function has strong nonlinear and multi-peak distribution in the solution space. Then, the multi-objective adaptive covariance matrix and chaotic search group algorithm (MOACCGA) is proposed to solve the multi-source energy optimization problem. The algorithm utilizes the evolutionary approach with an adaptive covariance matrix to adapt to the strong nonlinearity of the system. Additionally, it overcomes the problem of getting trapped in local optima within the solution space characterized by multiple peak distributions by employing chaotic search. Finally, the multi-source energy optimization method is validated through simulations. In each condition, multi-source energy optimization can save fuel consumption by 0.7% of the total fuel consumption and reduce thrust loss by 1.5% of the total thrust.

Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework

This research presents a comprehensive framework to enhance the cybersecurity and efficiency of smart urban energy systems by integrating cutting-edge technologies. Leveraging advanced evolutionary optimization techniques, specifically Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed system optimizes energy management and trading within urban infrastructure. Simultaneously, the integration of Internet of Things (IoT) devices enables real-time monitoring and decision-making, while blockchain technology ensures secure and transparent transactions. The study evaluates system performance by comparing it with traditional approaches, assessing key metrics such as convergence speed, exploration, exploitation, and cybersecurity. Results indicate that the proposed framework outperforms traditional systems in energy efficiency, cost-effectiveness, and cybersecurity metrics. This research offers valuable insights into integrating emerging technologies to address evolving challenges in smart urban energy systems. The findings highlight the potential of this innovative framework to revolutionize urban energy management, providing a foundation for more resilient, secure, and efficient smart cities. As the global urban landscape continues to evolve, this framework represents a strategic approach to meet the increasing demands for sustainable and secure urban energy solutions.

Optimization of Grating Coupler over Single-Mode Silicon-on-Insulator Waveguide to Reach < 1 dB Loss through Deep-Learning-Based Inverse Design

Grating couplers are essential components in silicon photonics that facilitate the coupling of light between waveguides and fibers. Optimization of the grating couplers to reach <1 dB loss when coupling to single-mode fibers (SMFs) has been reported in the literature, but this was based on silicon-on-insulator (SOI) waveguides supporting multi-modes. In this paper, using a deep-learning model combined with an inverse-design process, we achieve <1 dB losses for grating couplers implemented over single-mode SOI waveguides, i.e., a maximum efficiency of 80.5% (−0.94 dB) for gratings constrained with e-beam (EB) lithography critical dimension (CD), and a maximum efficiency of 77.9% (−1.09 dB) for gratings constrained with deep ultraviolet (DUV) lithography CD. To verify these results, we apply covariance matrix adaptation evolution strategy (CMA-ES) and find that while CMA-ES yields slightly better results, i.e., 82.7% (−0.83 dB) and 78.9% (−1.03 dB) considering e-beam and DUV, respectively, the spatial structures generated by CMA-ES are nearly identical to the spatial structures generated by the deep-learning model combined with the inverse-design process. This suggests that our approach can achieve a representative low-loss structure, and may be used to improve the performance of other types of nanophotonic devices in the future.

Load frequency and voltage control of two area interconnected power system using covariance matrix adaptation evolution strategy based Aquila optimization optimized fast fuzzy-fractional order tilt integral derivative controller

In this paper, an optimal tuning method is given for a Fast Fuzzy-Fractional Order Tilt Integral Derivative Controller (HFF-FOTIDC) used in the Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR) systems of a two-area interconnected power system. LFC is in charge of managing the frequency and real power flows in the system. AVR is in charge of maintaining the voltage profile and managing the flow of reactive power. In Area 1, a step disturbance is used to study how the system works in a dynamic way. By looking at the system's voltage, power flow through the tie lines, and frequency, the experts can figure out how the system will react. The proposed control techniques combine two well established control logic, which are fast fuzzy control logic and fractional order tilt integral derivative control logic. The main goal is to decrease voltage and frequency changes caused by step disturbances and return them to their normal values. The simulation results show how good the suggested system is by comparing it to systems that use traditional proportional integral controllers and traditional Integral controllers. By using the finest tuning method for the fast fuzzy-fractional order tilt integral derivative controller, the designed system has better performance than standard controllers at reducing fluctuations and getting the desired frequency and voltage responses. The parameters of the proposed controller are optimized by using Covariance Matrix Adaptation Evolution Strategy based Aquila Optimization (CMAESAO) optimization technique.

Covariance matrix adaptation evolution strategy based on ensemble of mutations for parking navigation and maneuver of autonomous vehicles

Previous works have demonstrated the use of human-inspired frameworks to address autonomous vehicle parking. In such frameworks, autonomous vehicle parking is formulated as a two-stage optimization problem where the first stage involves the generation of initial waypoints which are consequently used to reduce the autonomous parking task into a local motion planning task. In the second stage, the local motion planning is solved as a classical optimization problem. However, the overall algorithmic performance in the second stage depends on the planning horizon used to formulate the local planning task. In this work, we show first that the state-of-the-art algorithms often employed in the second stage fail with an increase in the planning horizon. Motivated by the need for algorithms that are scalable to different planning horizons, this work proposes Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with an ensemble of mutations for the second stage. Specifically, the proposed algorithm features an ensemble of Gaussian- and Cauchy-based mutations to facilitate an efficient blend of both exploitation and exploration that is crucial for both short and long horizons. Furthermore, to handle the associated constraints, Superiority of Feasible (SF) constraint handling technique is incorporated into the proposed algorithm. Performance analysis based on 24 parking missions conducted with planning horizons of 2 and 4 shows that the proposed algorithm is scalable to different planning horizons compared with three commonly employed state-of-the-art algorithms.

Predicting and analyzing the algal population dynamics of a grass-type lake with explainable machine learning

Algal blooms, exacerbated by climate change and eutrophication, have emerged as a global concern. In this study, we introduce a novel interpretable machine learning (ML) workflow tailored for investigating the dynamics of algal populations in grass-type lakes, Liangzi lake. Utilizing seven ML methods and incorporating the covariance matrix adaptation evolution strategy (CMA-ES), we predict algal density across three distinct time periods, resulting in the construction of a total of 30 ML models. The CMA-ES-CatBoost model consistently demonstrates superior predictive accuracy and generalization capability across these periods. Through the collective validation of various interpretable tools, we identify water temperature and permanganate index as the two most critical water quality parameters (WQIs) influencing algal density in Liangzi Lake. Additionally, we quantify the independent and interactive effects of WQIs on algal density, pinpointing key thresholds and trends. Furthermore, we determine the minimum combination of WQIs that achieves near-optimal predictive performance, striking a balance between accuracy and cost-effectiveness. These findings offer a scientific and economically efficient foundation for governmental agencies to formulate strategies for water quality management and sustainable development.

Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization

Solving real-world engineering optimization problems, such as automotive crashworthiness optimization, is extremely challenging, because the problem characteristics are oftentimes not well understood. Furthermore, typical hyperparameter optimization approaches that require a large function evaluation budget are computationally hindered, if the function evaluation is expensive, e.g., requires finite element (FE) simulation runs. In this paper, we propose an approach to characterize real-world expensive black-box optimization problems using the exploratory landscape analysis (ELA). Based on these landscape characteristics, we can identify test functions that are fast-to-evaluate and representative for hyperparameter optimization purposes. Focusing on 20 problem instances from automotive crashworthiness optimization, our results reveal that these 20 crashworthiness problems exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used black-box optimization benchmarking (BBOB) problem set. In fact, these 20 problem instances belong to problem classes that are distinct from the BBOB test functions based on the clustering results. Further analysis indicates that, as far as the ELA features concern, they are most similar to problem classes of tree-based test functions. By analyzing the performance of two optimization algorithms with different hyperparameters, namely the covariance matrix adaptation evolutionary strategy (CMA-ES) and Bayesian optimization (BO), we show that the tree-based test functions are indeed representative in terms of predicting the algorithm performances. Following this, such scalable and fast-to-evaluate tree-based test functions have promising potential for automated design of an optimization algorithm for specific real-world problem classes.

An Efficient Differential Grouping Algorithm for Large-Scale Global Optimization

Cooperative co-evolution (CC) is a practical and efficient evolutionary framework for solving large-scale global optimization problems (LSGOPs). The performance of CC depends on how variables are being grouped and can be improved through guided variable decomposition for various optimization problems. However, achieving a proper variable decomposition is computationally expensive. This paper proposes an effective yet efficient differential grouping (EDG) method to reduce the associated computational cost. Our method exploits the historical interrelationship information of previous variable groups to examine interactions between the remnant variable groups. This allows us to spend less computing resources without compromising the accuracy of the final grouping result. Our proposal utilizes the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, in conjunction with EDG, to solve LSGOPs. Further, to reduce time complexity and improve the stability of CMA-ES, we substitute the complex matrix decomposition step with simpler matrix operations to compute the square root of the covariance matrix. Results from our experiments and analysis indicate that EDG is a competitive method to solve LSGOPs and improve the performance of CC. The proposed schemes significantly enhance the searchability of CMA-ES compared to the other large-scale variants of CMA-ES and state-of-the-art large-scale optimizers. Moreover, our EDG could be integrated with evolutionary optimizers of different flavors like Differential Evolution (DE).

Trajectory Correction of the Rocket for Aerodynamic Load Shedding Based on Deep Neural Network and the Chaotic Evolution Strategy With Covariance Matrix Adaptation

Trajectory Correction of the Rocket for Aerodynamic Load Shedding Based on Deep Neural Network and the Chaotic Evolution Strategy With Covariance Matrix Adaptation

A complex system health state assessment method with reference value optimization for interpretable BRB

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment.

Covariance matrix adaptation evolution strategy based on correlated evolution paths with application to reinforcement learning

Proven as an efficient population-based optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) features two evolution paths, one to update the covariance matrix and the other to adapt its mutation strength. Considering the time and space complexity of CMA-ES, there are several attempts in the literature to realize a single-path algorithm. However, such attempts require altering the original structure of CMA-ES and consequently eliminating some vital features crucial to the overall algorithm performance. In this paper, we show that the two evolution paths of CMA-ES are highly correlated and one can be expressed in terms of the other thus reducing the computational cost of the algorithm while preserving the original algorithmic framework. Based on experimental studies conducted using 30 functions from the IEEE CEC 2014 benchmark suite, the proposed algorithm shows comparable results with the standard CMA-ES as well as five other state-of-the-art CMA-ES variants. Furthermore, it is shown that the proposed algorithm can be applied to policy search in Deep Reinforcement Learning (DRL). Performance results based on selected DRL problems from different application domains prove the efficiency of the proposed algorithm compared to other population-based algorithms often applied for policy search in DRL.

Integration of sales, inventory, and transportation resource planning by dynamic-demand joint replenishment problem with time-varying costs

This paper addresses the joint replenishment problem in the context of sales and operations planning. Unlike traditional ones that assume static demands, unit costs, and transportation capacity, we consider dynamic-deterministic demands from sales plans and time-varying unit costs and transportation capacity to deal with the recent logistics conditions. We then introduce the capacitated dynamic-demand joint replenishment problem with time-varying costs. To efficiently solve this problem, a three-phase approach is proposed: (1) simplifying the problem and determining ideal inventory quantities using mixed integer linear programming, (2) estimating policy variables for each item using the covariance matrix adaptation evolution strategy, and (3) updating the ideal inventory quantities based on evaluated shortages until all demands are satisfied. Our method outperforms conventional approaches with at least 12 times faster solution runtimes in tests with up to 100 items. We also obtain the insight that the cost-efficient replenishment plan changes according to the increase rate of the unit transportation cost.

Transfer learning based covariance matrix adaptation for evolutionary many-objective optimization

Evolutionary Algorithms (EA) have proven successful in solving Multi- and Many-objective Optimization Problems (MOPs/MaOPs) in numerous application areas. However, the various linkages among the decision variables may have posed a challenge for traditional evolutionary algorithms for solving such problems. In this paper, a transfer learning based covariance matrix adaptation algorithm, shortened as TL-M2M-CMA, is proposed to handle MOPs/MaOPs with complex decision variable linkage. In TL-M2M-CMA, the search population is first decomposed into a set of subpopulations and each subpopulation corresponds to a specific part of the Pareto-optimal Front (PF) of the MOP/MaOP. During the search, a covariance matrix adaptation process is utilized to learn the linkage among the decision variables, and then the covariance matrix is incorporated into the crossover and mutation operators for better efficiency. To facilitate performance comparison, a set of scalable MOP test instances with various linkage complexities is constructed for experimental studies. The performance of the proposed TL-M2M-CMA on these constructed instances is verified by comparing it to the performance of five state-of-the-art EA methods.

Automatic Temperature Parameter Tuning for Reinforcement Learning Using Path Integral Policy Improvement.

In this article, we propose a novel variant of path integral policy improvement with covariance matrix adaptation ( [Formula: see text] - [Formula: see text] ), which is a reinforcement learning (RL) algorithm that aims to optimize a parameterized policy for the continuous behavior of robots. [Formula: see text] - [Formula: see text] has a hyperparameter called the temperature parameter, and its value is critical for performance; however, little research has been conducted on it and the existing method still contains a tunable parameter, which can be critical to performance. Therefore, tuning by trial and error is necessary in the existing method. Moreover, we show that there is a problem setting that cannot be learned by the existing method. The proposed method solves both problems by automatically adjusting the temperature parameter for each update. We confirmed the effectiveness of the proposed method using numerical tests.

An Enhanced Genetic Algorithm using Directional-Based Crossover and normal mutation For Global Optimization Problems

Global optimization has been employed in many practical modeling processes. Using gradient methods to solve optimization problems may be computationally inefficient and time-consuming, particularly when convexity or differentiability is not guaranteed. On the other hand, nature-inspired techniques offer an effective gradient-free approach for solving complex, non-convex, or non-differentiable problems. Genetic algorithms are one of the most effective and widely used nature-inspired techniques. However, canonical genetic algorithms do not always guarantee convergence to the optimum point owing to the stochastic nature of the genetic operators, and typically require more work to ensure convergence and increase performance. Improving the genetic operators remains an open issue and usually involves a trade-off between the speed of convergence and searchability. In this study, we propose an enhanced genetic algorithm that relies on directional-based crossover and normal mutation operators to increase the speed of convergence while preserving searchability. The proposed algorithm is evaluated using a set of 40 typical benchmark functions in two dimensions. In addition, to examine its performance at higher dimensions, 16 functions from the test set were tested at 10 and 100 dimensions. The evaluation results of the proposed algorithm are compared to the outcomes of three modern optimization algorithms, namely (Whale optimization algorithm, Teacher-Learner based algorithm, and Covariance matrix adaptation evolution strategy). The results revealed that the proposed algorithm outperformed the conventional algorithms at lower dimensions in all test functions and showed a relatively better performance than the other algorithms at higher dimensions.

Clinamen2: Functional-style evolutionary optimization in Python for atomistic structure searches

Clinamen2 is a versatile functional-style Python implementation of the covariance matrix adaptation evolution strategy (CMA-ES) utilizing Cholesky decomposition. On top of a problem-agnostic core algorithm, the software package offers a suite of utilities and library code enabling applications to important atomistic structure searches. Features include massively distributed computation and the BI-Population restart scheme. This article details the general code structure and introduces examples that illustrate some relevant applications for the materials science and chemistry worlds, including interfacing to density-functional-theory codes and machine-learned surrogate models. The functional design renders the code modular and adaptable, and makes the creation of interfaces to other atomistic software straightforward. Program summaryProgram Title: Clinamen2CPC Library link to program files:https://doi.org/10.17632/x7syr2txsd.1Developer's repository link:https://github.com/Madsen-s-research-group/clinamen2-public-releasesCode Ocean capsule:https://codeocean.com/capsule/4950229Licensing provisions: Apache-2.0Programming language: PythonSupplementary material:Nature of problem: Find optimal atomistic structures retaining full flexibility in the choice of the optimization target, the methodological approach and its implementation (e.g. CPU- vs. GPU-heavy calculations). Enable interfacing with relevant software, including but not limited to density-functional-theory (DFT) codes and machine-learning (ML) solutions.Solution method: The covariance matrix adaptation evolution strategy (CMA-ES) algorithm is implemented using Cholesky decompositions for efficiency. The core algorithm and application examples for specific problems are implemented in functional-style Python free of side effects, with data classes to keep track of the state of the evolution. Dask is used for job control to enable highly distributed workflows. Advanced strategies like BI-Population CMA-ES are easy to implement and illustrated in the examples.

Multi-objective optimization of permanent magnet motors using deep learning and CMA-ES

This paper proposes a multi-objective optimization method for permanent magnet motors using a fast optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and deep learning. Multi-objective optimization with topology optimization is effective in the design of permanent magnet motors. Although CMA-ES needs fewer population size than genetic algorithm for single objective problems, this is not evident for multi-objective problems. For this reason, the proposed method generates training data by solving the single-objective optimization multiple times using CMA-ES, and constructs a deep neural network (NN) based on the data to predict performance from motor images at high speed. The deep NN is then used for fast solution of multi-objective optimization problems. Numerical examples demonstrate the effectiveness of the proposed method.

Performance and Techno-Economic Analysis of Optimal Hybrid Renewable Energy Systems for the Mining Industry in South Africa

This paper presents an exploration of the potential of hybrid renewable energy systems (HRESs), combining floating solar photovoltaics (FPV), wind turbines, and vanadium redox flow (VRF) battery energy storage systems (BESSs) to expedite the transition from conventional to renewable energy for the mining sector in South Africa. The feasibility study assesses how to enhance the overall efficiency and minimize greenhouse gas emissions from an economic standpoint by using the Hybrid Optimization of Multiple Energy Resources (HOMER) grid software version 1.11.1 and PVsyst version 7.4. Furthermore, the BESS Covariance Matrix Adaptation Evolution Strategy (CMA-ES) dispatch algorithm is proposed to make the most of the battery storage capacity and capability, aligning it with the dynamic energy demand and supply patterns of an HRES. The proposed HRES includes a highly efficient SFPV with a performance ratio of 0.855 and an annual energy production of 15,835 MWh; a wind turbine (WT) operating for 2977 h annually, achieving a 25% wind penetration rate; and a dynamic VRF-BESS with a 15,439 kWh life throughput and a 3 s dispatch response time. This HRES has a CapEx of R172 million, a 23.5% Internal Rate of Return (IRR), and an investment payback period of 4.9 years. It offers a low Levelized Cost of Energy (LCoE) at 4.27 R/kWh, a competitive Blended Cost of Energy (BCoE) at 1.91 R/kWh, and a positive net present cost (NPC), making it economically advantageous without external subsidies. Moreover, it annually reduces CO2 emissions by 1,715,468 kg, SO2 emissions by 7437 kg, and NOx emissions by 3637 kg, contributing to a significant environmental benefit.