Matrix Adaptation Evolution Strategy Algorithm Research Articles

The spds*p*+Δ tight binding model for 3C-SiC

In this study, we propose a novel model for crystals containing second-period elements. Adopting the sp3d5s*p*3+∆ tight-binding model, which accounts for the influence of spin–orbit coupling on 3C-SiC, including carbon atoms as second-period elements, we calculate the energy band structure. The Slater–Koster parameters used in the calculations were optimized to experimental values, such as the band gap energy and effective mass, using the covariance matrix adaptation evolution strategy algorithm, a black-box optimization method suitable for such applications. The optimized energy band structure accurately represents the experimental data, confirming the significant impact of p* orbitals near the band gap through projected density of states calculations.

Structural optimisation for controlled deflections of additively manufactured single material beams

Closely controlling the mechanical behaviour and characterization of the deflection of a beam structure is a well-known and widely studied engineering problem. The progress in additive manufacturing methods and the possibilities to closely control the material property variations with the controlled placement of materials further widen the opportunities to achieve given beam deflection criteria. The multi-material additive manufacturing solutions suffer from the lack of real engineering material options, and the quality and performance of the printed parts are usually unsuitable for producing functional parts. A novel cellular structured solution is proposed here, which utilises optimisation of geometries of individual cells of a single material structured beam to obtain deflection profiles closely matched with preset conditions under different loading conditions. The cellular geometry of the structured beam is continually altered for searching and converging on the optimal structure of the cells by the covariance matrix adaptation evolution strategy algorithm in an iterative manner. The optimised beam structures could also be physically produced with single material additive manufacturing methods and the experimental and numerical beam deflection responses correlated closely.

Differential evolutionary cuckoo-search-integrated tabu-adaptive pattern search (DECS-TAPS): a novel multihybrid variant of swarm intelligence and evolutionary algorithm in architectural design optimization and automation

Abstract One of the critical limitations in architectural design optimization (ADO) is slow convergence due to high-dimensional and multiscale variables. For the rapid and optimal digital prototyping of architectural forms, this paper proposes a novel metaheuristic optimization technique that hybridizes standard low-level algorithms: the differential evolutionary cuckoo-search-integrated tabu-adaptive pattern search (DECS-TAPS). We compared DECS-TAPS to 10 major standard algorithms and 31 hybrids through 14 benchmark tests and investigated multi-objective ADO problems to prove the computational effectiveness of multiple algorithm hybridization. Our findings show that DECS-TAPS is vastly efficient and superior to the covariance matrix adaptation evolution strategy algorithm in multifunnel and weak structural functions. The global sensitivity analysis demonstrated that integrating multiple algorithms is likely conducive to lowering parameter dependence and increasing robustness. For the practical application of DECS-TAPS in building simulation and design automation, Zebroid—a Rhino Grasshopper (GH) add-on—was developed using IronPython and the GH visual scripting language.

Optimization of a Photonic Crystal Nanocavity Using Covariance Matrix Adaptation Evolution Strategy

H0-type photonic crystal nanocavities hold high quality factors <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> and quite small cavity mode volumes. This study finds their ultrahigh <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> structures, which allow stable operation as a nanolaser even with fabrication-induced disordering. Previously, we generated a neural network model for predicting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> s, searched for a high- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> structure and its slotted version, and found those showing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> = 1,140,000 and 91,600, respectively. These values were an order of magnitude higher than those obtained by manual optimizations. However, further improvement above these values was saturated because of the insufficient accuracy of the neural network model at the high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> regime. Instead of applying the model, we repeated directly calculating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> s, implementing a covariance matrix adaptation evolution strategy algorithm to search structures in this study. Consequently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> values were increased up to 14,500,000 and 741,000 while consuming shorter calculation time. We also confirmed that these structures significantly improve robustness against structural disordering.

Nonlinear Acoustic Tomography for Measuring the Temperature and Velocity Fields by Using the Covariance Matrix Adaptation Evolution Strategy Algorithm

Accurate measurement of temperature and velocity fields in combustion and flow diagnostic plays an important role in analyzing the heat transfer mechanism and ensuring the safe operation of equipment. In this work, considering the refraction effect of sound waves, temperature and velocity fields are reconstructed simultaneously based on the nonlinear acoustic tomography (NAT) by using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. In the forward problem, the fast two-point ray-tracing algorithm is introduced to track curved sound waves more efficiently. Due to the sparse measurement signals caused by the limited space and curved acoustic paths, Tikhonov regularization is introduced as the constraint of smoothing prior information to reduce the ill-posedness of the problem. The influence of the velocity regularization parameter and temperature regularization parameter on simultaneously visualizing the temperature and corresponding velocity fields are investigated, respectively. Then the selection strategies of these two regularization parameters are given, which are proved to have high selection efficiency. On this basis, inhomogeneous temperature and velocity fields are reconstructed simultaneously by using the CMA-ES algorithm, simulated annealing, and genetic algorithms. By solving the inverse problem of the NAT, which is highly nonlinear and ill-posed, the feasibility and effectiveness of the CMA-ES algorithm in the simultaneous reconstruction of velocity and temperature fields are confirmed. The proposed method for visualizing the temperature and velocity fields will supply key feedback for the combustion process control.

Reliability assessment of train control and management system based on evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm

The reliability assessment of train control and management system (TCMS) is essential for the condition monitoring of high-speed train. Different from other general complex systems, the TCMS has the characteristics of multi-system unit, strong coupling and multiple factors. Considering the special system operating environment and high safety requirements of high-speed train. In this paper, for the reliability assessment of TCMS, we propose a new quantitative model based on the evidential reasoning rule and covariance matrix adaptation evolution strategy algorithm, the proposed model offers the following advantages: it has a strong modeling capability for the TCMS reliability, it has an interpretable model assessment process, it can describe the assessment result under probabilistic uncertainty and ignorance uncertainty, and it possesses considerable robustness. To make the model interpretable, an assessment hierarchy is established for the TCMS; to improve model robustness, weights interval is applied to replace the trained weights as the model weights. Several traditional methods are compared with the proposed model to demonstrate its performance, the results show that the proposed model has a better training accuracy. Moreover, a case study is conducted to verify the model’s functional feasibility.

DNA Pool Analysis-based Forgery-Detection of Dairy Products

Food integrity and food safety have received much attention in recent years due to the dramatic increasing number of food frauds. In this article we focus on the problem of dairy products traceability. In particular, we propose an automatic forgery detection system able to detect frauds in milk and cheese. We investigate the use of Short Tandem Repeats analysis data, processed by a Covariance Matrix Adaptation Evolution Strategy algorithm in order to evaluate a traceability score between the products and their producer, and to highlight possible adulterations and inconsistencies. To demonstrate the usability of the proposed heuristic algorithm in a real setup, we also present the results collected from two real Italian farms.

A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network

In the technology of wireless sensor network (WSN), wireless sensor fault diagnosis based on fusion data analysis has attracted attention in the wireless sensor field. It can detect and correct the faults of sensor nodes in time to improve the accuracy of sensor data fusion. In this paper, the data characteristics of WSN are analyzed, and a method is proposed for fault diagnosis of WSN based on a belief rule base (BRB) model. First, the sensor fault diagnosis process is described based on the characteristics of a wireless sensor in WSN. Then, the characteristics of sensors are analyzed from the aspects of time, space, and attributes. Finally, a fault diagnosis model is proposed based on the hierarchical BRB model. To make the results more accurate, a covariance matrix adaptation evolution strategy algorithm is used to optimize the initial parameters of the proposed model. A case study using the Intel lab data set of sensors is designed to verify the effectiveness of the proposed model. The results show that the proposed method is effective in fault diagnosis of WSN.

An Efficient Improvement of CMA-ES Algorithm for the Network Security Situation Prediction

An improved covariance matrix adaptation evolution strategy algorithm (CMA-ES) is proposed and it is used to train the forecasting model of the network security situation in this paper. A new recombination strategy which adds a heuristic component is developed in the improved CMA-ES algorithm, and the search speed is accelerated. The experi- mental results show that, compare with original algorithm and its variants, the improved CMA-ES algorithm can greatly increased the search speed in high dimensional problems. The improved CMA-ES algorithm is an efficient evolutionary algorithm which can be applied to the network security situation prediction.

Time-history analysis based optimal design of space trusses: the CMA evolution strategy approach using GRNN and WA

In recent years, the need for optimal design of structures under time-history loading aroused great attention in researchers. The main problem in this field is the extremely high computational demand of time-history analyses, which may convert the solution algorithm to an illogical one. In this paper, a new framework is developed to solve the size optimization problem of steel truss structures subjected to ground motions. In order to solve this problem, the covariance matrix adaptation evolution strategy algorithm is employed for the optimization procedure, while a generalized regression neural network is utilized as a meta-model for fitness approximation. Moreover, the computational cost of time-history analysis is decreased through a wavelet analysis. Capability and efficiency of the proposed framework is investigated via two design examples, comprising of a tower truss and a footbridge truss.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Application of covariance matrix adaptation–evolution strategy to optimal control of hepatitis B infection

To avoid the cirrhosis and liver cancer, antiviral treatment for chronic hepatitis is necessary. In the literature, several mathematical models have been used to describe the dynamics of viral infections. In addition, several control strategies have been reported in the literature to deal with optimal antiviral therapy problem of infectious diseases. In this paper, three controller structures with optimized parameters using covariance matrix adaptation–evolution strategy algorithm are proposed for optimal control of basic hepatitis B virus (HBV) infection dynamical system. The first structure is an optimized neural-type sigmoid-based closed-loop controller, which is a nonlinear feedback controller. The second structure is an optimized open-loop time-based fuzzy controller in which the control input is approximated using the mixture of Gaussian membership functions. Finally, an optimized closed-loop fuzzy controller is used as the third control structure. After designing the controllers, some parameters of the HBV infection model are considered to be unknown and the robustness of the controllers is studied. Experimental results show that the optimized neural-type sigmoid-based closed-loop controller has the best performance in terms of healthy hepatocytes and free HBVs concentration among the investigated controllers and the optimized closed-loop fuzzy controller is the best in terms of minimum mean control input signal that is the drug usage. Concerning the robustness, the optimized neural-type sigmoid-based closed-loop controller has the best performance.

Multiobjective Decentralized Congestion Management Using Modified NSGA-II

This paper proposes a model for the decentralized multiobjective congestion management problem in the deregulated forward power market by considering conflicting objectives of the maximization of social welfare and the minimization of emission impacts. An elitist evolutionary multiobjective optimization algorithm called Modified Non-dominated Sorting Genetic Algorithm II (MNSGA-II) with controlled elitism and a dynamic crowding distance is applied. To validate the model, an IEEE 30-bus test system with three multilateral transactions is considered. The valve-point effect is included in the social welfare function. Voltage and reactive power effects are also incorporated. The closeness of the results of multiobjective decentralized and centralized congestion management demonstrates the validity of the proposed multiobjective decentralized model. The proposed model provides a set of alternative solutions to the market participants to manage congestion. The effectiveness of the proposed approach is demonstrated by comparing the obtained Pareto front with a reference Pareto front generated with multiple runs of the Covariance Matrix Adapted Evolution Strategy algorithm with respect to minimum spacing, diversity and convergence metric performance measures.