Adaptation Evolution Strategy Research Articles

Does architectural design require single-objective or multi-objective optimisation? A critical choice with a comparative study between model-based algorithms and genetic algorithms

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions.

Aviation fuel pump health state assessment based on evidential reasoning and random forests

AbstractAs the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

Dual stress factors adaptive evolution for high EPA production in Schizochytrium sp. and metabolomics mechanism analysis.

Eicosapentaenoic acid (EPA) is a vital ɷ-3 polyunsaturated fatty acid (PUFA) for human body with various physiological functions. In this study, we proposed an adaptive evolutionary strategy based on high-temperature and high-oxygen two-factor stress to increase the EPA production capacity of Schizochytrium. High-temperature stress was used to increase EPA yield, and high oxygen was implemented to continuously stimulate cell growth and lipid accumulation. The biomass and EPA production of ALE-D50 reached 35.33g/L and 1.54g/L, which were 43.85% and 71.11% higher than that of the original strain, respectively. Lower in vivo reactive oxygen species levels indicated that the evolved strain possessed stronger antioxidant activity. Liquid chromatography-mass spectrometry metabolomics showed that enhanced glucose consumption and glycolysis metabolism, as well as a weakened tricarboxylic acid cycle and reduced amino acid metabolic tributaries in the evolved strain, might be associated with increased growth and EPA synthesis. Finally, the lipid production and EPA production in a fed-batch fermentation were further increased to 48.93g/L and 3.55g/L, improving by 54.30% and 90.86%, respectively. This study provides a novel pathway for promoting EPA biosynthesis in Schizochytrium.

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.

Waveform design through the trade-off relationship between the MI criterion and the SINR criterion

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.

Key Contributions of the Overexpressed Plutella xylostella Sigma Glutathione S-Transferase 1 Gene (PxGSTs1) in the Resistance Evolution to Multiple Insecticides.

The overexpression of insect detoxification enzymes is a typical adaptive evolutionary strategy for insects to cope with insecticide pressure. In this study, we identified a glutathione S-transferase (GST) gene, PxGSTs1, that exhibited pronounced expression in the field-resistant population of Plutella xylostella. By using RNAi (RNA interference), the transgenic fly models, and quantitative real-time polymerase chain reaction (RT-qPCR) methods, we confirmed that the augmented expression of PxGSTs1 mediates the resistance of P. xylostella to various types of insecticides, including chlorantraniliprole, novaluron, λ-cyhalothrin, and abamectin. PxGSTs1 was found to bolster insecticide resistance in two ways: direct detoxification and enhancing antioxidative defenses. In addition, our findings demonstrated that pxy-miR-8528a exerts a pivotal influence on forming insecticide resistance in P. xylostella by downregulating PxGSTs1 expression. In summary, we elucidated the multifaceted molecular and biochemical underpinnings of PxGSTs1-driven insecticide resistance in P. xylostella. Our results provide a new perspective for understanding the insecticide resistance mechanism of P. xylostella.

Multiobjective Differential Evolution Algorithm Balancing Multiple Stakeholders for Low-Carbon Order Scheduling in E-Waste Recycling

Order scheduling is an important part of the e-waste recycling process, which can influence the quantity and efficiency of the recycling. With the sustainable development of e-waste recycling, low-carbon order scheduling becomes a significant and challenging reverse logistics scheduling problem. However, it is difficult to obtain an effective low-carbon order schedule considering the conflicting interests of the multiple stakeholders, including enterprises, drivers, customers, and governments. To address this issue, a multi-objective order scheduling model (MOOSM) and a multi-objective differential evolution algorithm balancing multiple stakeholders (MODE-MS) are proposed in this paper. First, to embody the interests of different stakeholders, three time-dependent key variables are calculated by the road congestion and vehicle load, including the velocity, travelling time and carbon emission. Second, with the above key variables, a five-objective order scheduling model is formulated to describe the low-carbon order scheduling problem in e-waste recycling. Third, for solving the MOOSM, a multi-objective differential evolution algorithm based on an adaptive evolutionary search strategy is developed to obtain the low-carbon and stakeholders satisfied scheduling schemes. The experimental results validate the feasibility of MOOSM and the effectiveness of MODE-MS. By comparing with four state-to-art algorithms, the advantages of the proposed MODE-MS are further demonstrated in solving the low-carbon order scheduling.

Adaptive evolutionary strategies of microbes to manganese in nitrogen-culturing sludge

Human activities have significantly increased the accessibility of nitrogen and manganese, which has had an impact on the evolution of microorganisms. To investigate the effect of increasing manganese accessibility on microbial nitrogen cycle, Mn-HA (Manganese -Humus chelates), a naturally occurring compound in the form of organochelate, was introduced into the conventional nitrification-denitrification process. The findings revealed that the addition of Mn-HA significantly facilitated the removal of ammonium and nitrate potentially by Mnammox (anaerobic ammonium oxidation mediated by Mn (IV)-oxide reduction) and Mn-based autotrophic denitrification. Metagenomic analyses suggested the presence of manganese-redox microorganisms involved in nitrification and denitrification, indicating the occurrence of manganese redox-coupled nitrogen metabolism processes. Additionally, the production and consumption of bio-MnOx (bio-Manganese Oxides) were observed in conjunction with denitrification and nitrification. The production of unequal amounts of bio-MnOx suggested that bio-MnOx may have been deposited in the sediment. This was confirmed by the BCR sequential extraction experiment. Coupling Mn metabolism with N metabolism is an adaptive strategy for nitrogen metabolizing microorganisms. For Mn-sensitive populations, they gain respite from Mn-rich environments by adopting strategies such as manganese oxidation, regulating manganese transport, pumping out intracellular manganese and evolving other manganese resistance genes.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems

Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which are considered challenging. The teen metaheuristic algorithms employed in this study are, namely, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization Algorithm (BBO), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Cuckoo Search algorithm (CS), Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). The efficiency of these algorithms is evaluated on teen popular engineering classical design problems using the solution quality and convergence analysis, which verify the applicability of these algorithms to engineering classical constrained design problems. The experimental results demonstrated that all the algorithms provide a competitive solution.

Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization

Flooding is one of the most common natural hazards that have extremely detrimental consequences. Understanding which areas are vulnerable to flooding is crucial to addressing these effects. In this work, we use machine learning models and Automated machine learning (AutoML) systems for flood susceptibility mapping in Kerala, India. In particular, we used a three-dimensional convolutional neural network (CNN) architecture for this purpose. The CNN model was assisted with hyperparameter optimization techniques that combine Bayesian optimization with evolutionary algorithms like differential evolution and covariance matrix adaptation evolutionary strategies. The performances of all models are compared in terms of cross-entropy loss, accuracy, precision, recall, area under the curve (AUC) and kappa score. The CNN model shows better performance than the AutoML models. Evolutionary algorithm-assisted hyperparameter optimization methods improved the efficiency of the CNN model by 4 and 9 percent in terms of accuracy and by 0.0265 and 0.0497 with reference to the AUC score.

Multi-objective optimization for fast charging design of lithium-ion batteries using constrained Bayesian optimization

Fast charging of lithium-ion battery accounting for both charging time and battery degradation is key to modern electric vehicles. The challenges of fast charging optimization are (i) the high dimensionality of the space of possible charging protocols while the experiment budget is often limited; and (ii) the limited quantitative description of battery capacity fade mechanisms. This article proposes a data-driven multi-objective charging approach to minimize charging time while maximizing battery cycle life, in which a Chebyshev scalarization technique is used to transform the multi-objective optimization problem into a group of single objective problems, and a constrained Bayesian optimization (BO) is then utilized to effectively explore the parameter space of charging current as well as handle the constraint of charging voltage. Moreover, continuous-varied-current charging protocols are introduced into the proposed charging optimization approach by the utilization of polynomial expansion technique. The effectiveness of the proposed charging approach is demonstrated on a porous electrode theory-based battery simulator. The results show that the proposed constrained BO-based approach possesses superior charging performance and higher sample efficiency, compared with the state-of-the-art baselines including constrained optimization by linear approximations (COBYLA) and covariance matrix adaptation evolutionary strategy (CMA-ES). In addition, the increase in the charging performance and its uncertainty with an increasing number of degrees of freedom used in charging protocols is discussed.

A novel waypoint guidance and adaptive evolution strategy for unmanned aerial vehicle 3D route planning

In the field of unmanned aerial vehicles (UAVs) mission planning, autonomous route planning in complex 3D environments represents a crucial aspect. To enhance the utilization of valuable information and increase search efficiency when solving path planning problems in 3D environments, a novel UAV route planning algorithm named WG-GWO is proposed with a waypoint guidance and an adaptive evolution strategy. Initially, we introduce a novel waypoint extraction method based on cubic B-spline to harness crucial information from individuals eliminated during the evolutionary process. Subsequently, an adaptive evolutionary direction selection strategy is introduced, which utilizes historical information and a differential operator to improve the search ability. The WG-GWO also incorporates Le´vy flight to prevent premature convergence to local minimums. Simulation results validate the effectiveness of the proposed algorithm, demonstrating that WG-GWO outperforms several existing algorithms in generating feasible routes and achieving faster convergence speeds across various scenarios. These results suggest that WG-GWO holds potential for practical engineering applications.

Evaluation of a Behavioral Test for Sickness Behavior Associated with Fusarium Mycotoxin Ingestion in Female Beagle Dogs (Canis familiaris).

Animals exhibit behavioral changes during illness, including lethargy, anorexia, fever, adipsia, and anhedonia, which are believed to comprise an adaptive evolutionary strategy. Exploratory and social behaviors generally decrease during illness, but behavioral changes of dogs during illness have not been described. The objective of this study was to evaluate a novel canine behavior test during subclinical illness induced by dietary Fusarium mycotoxin. Twelve mature female beagle dogs received 3 treatment diets: a control diet (control), a diet formulated with grains contaminated with Fusarium mycotoxin (toxin), and the toxin diet together with a toxin binding agent (binder). All dogs received each diets for 14 d in a Latin square design with a 7-d washout period between diet trials. The test consisted of individually releasing dogs into the center aisle of the housing room for 4 min per day, during which interactions with familiar dogs in adjacent kennels were recorded by an observer outside the room who was blind to treatment groups. Total interactions, orientation, and attempted physical contact with other dogs were less frequent during the toxin and binder diet treatments. Conversely, frequencies of physical proximity and olfactory contact with familiar dogs in adjacent kennels were not associated with diet. In conclusion, induction of subclinical gastrointestinal illness influenced aspects of social interactions in beagle dogs. A clinical assessment sheet integrating these findings was developed to aid in early identification of subclinical illness in research dogs based on behavior.

Irrigation optimization in green asparagus (Asparagus officinalis L.) using the AquaCrop model and evolutionary strategy algorithms

AbstractAppropriate irrigation water management has attracted considerable attention because it is crucial to guarantee water and food security worldwide. This study aims to solve a water usage optimization problem for asparagus farming using the Deficit Irrigation Toolbox (DIT) optimization tool and AquaCrop‐OS model. Seven irrigation strategies were planned to assess the yield response of asparagus spears in the region near Caborca, Sonora, Mexico. Three of them were defined according to the usual practices of conventional irrigation, and the other four involved the optimization approach. Crop water productivity responses to 750, 1000, 1250 and 1500 mm of supplied water for the crop growing season were generated and assessed for each strategy. Using the irrigation optimization strategy, which involved a defined decision table using the covariance matrix adaptation evolutionary strategy, resulted in increased water productivity, making it the best solution to the optimization problem. A spear yield of 11.3–12.1 t ha−1 was obtained using an irrigation depth of 1200 mm, thus resulting in a reduced irrigation depth of 580–615 mm (>30%) for the 2019 growing season and 130–165 mm (±10%) for the 2020 growing season. The novelty of the study is that by formulating a dynamic optimization problem using the AquaCrop‐OS model and DIT, it is demonstrated that the water currently used for irrigation of asparagus crops can be reduced by up to 30%.

Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min–Max Optimization and Its Application to Berthing Control Tasks

In this study, we consider a continuous min–max optimization problem min x ∈ 𝕏 max y ∈ 𝕐 f ( x, y ) whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective function F ( x ) = max y ∈ 𝕐 f ( x, y ) directly using a covariance matrix adaptation evolution strategy in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation mechanism. We develop two variants of worst-case ranking approximation combined with a covariance matrix adaptation evolution strategy and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex–concave function and the interaction between x and y is strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex–concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.

Coupling covariance matrix adaptation with continuum modeling for determination of kinetic parameters associated with electrochemical CO2 reduction

Coupling covariance matrix adaptation with continuum modeling for determination of kinetic parameters associated with electrochemical CO2 reduction

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.

A new complex system fault detection method based on belief rule base for unreliable interval values

Failures to equipment such as milling machines and inertial navigation systems (INSs) can affect their normal operation, resulting in economic losses and personal injury in severe cases. Therefore, fault detection is of great importance. Belief rule base (BRB) is an expert system that plays an important role in fault detection. The traditional BRB has some problems in the explosion of the number of combination rules, the process of model inference, and the process of parameter optimization. To better deal with the above problems, this paper proposes a complex system fault detection method based on an interval-valued BRB fault detection interval-valued (FDIV) and provides the construction and inference process of the method. In the method construction, the form of interval value and disjunction rules are introduced to solve the problem of the number explosion of combination rules, the indicator reliability is added to improve the accuracy of the method, and a new calculation method of rule availability is proposed. In the inference process, twice fusions are made based on evidence reasoning (ER) analysis algorithm and ER rule algorithm respectively to deal with the interval uncertainties. Moreover, the proposed FDIV method is optimized by the projection covariance matrix adaptive evolutionary strategy algorithm projection covariance matrix adaptive evolutionary strategy (P-CMA-ES). Finally, the effectiveness of the proposed method was verified through the research on milling fault detection and the experimental verification of INS fault detection. The superiority of the model was also confirmed through comparative experiments.

GCP-HOLO: Generating High-Order Linkage Graphs for Path Synthesis

Abstract One degrees-of-freedom (1DOF) linkages are persistent in mechanical systems. However, designing linkages to follow a desired path, known as path synthesis, is challenging due to non-linearities, combinatorial nature, and strict geometric constraints. Current state-of-the-art algorithms cannot scale well to linkages with higher-order linkage graphs, which are required to satisfy more complicated paths for new mechanical systems, such as hopping and flying robots. One reason for this is that state-of-the-art algorithms spend the majority of the time exploring constraint-violating designs. This work uses an Assur group 0DOF linkage as a graph grammar rule to modify both linkage graph and spatial parameters, ensuring all designs are valid 1DOF linkages. Using this graph grammar, this paper formulates linkage path synthesis as a tree search and uses a deep reinforcement learning (DRL) agent to search the space of kinematically feasible planar 1DOF linkages. This paper introduces a method using a graph convolution policy for high-order linkage graph optimization (GCP-HOLO). An anytime algorithm, GCP-HOLO outputs linkages with 1–8 loops (4–16 bars) efficiently. When comparing the GCP-HOLO formulation to a recent state-of-the-art paper that solves a mixed integer conic program, GCP-HOLO generates sets of solutions of varying linkage complexities to eight test trajectories in a quarter of the time. Extending GCP-HOLO with a global node optimization, such as covariance matrix adaptation evolutionary strategy, the results quickly converge to finding better solutions for 4/8 tests, with the whole pipeline capable of a 13X speed increase.

Fault Diagnosis for Body-in-White Welding Robot Based on Multi-Layer Belief Rule Base

Fault diagnosis for body-in-white (BIW) welding robots is important for ensuring the efficient production of the welding assembly line. As a result of the complex mechanism of the body-in-white welding robot, its strong correlation of components, and the many types of faults, it is difficult to establish a complete fault diagnosis model. Therefore, a fault diagnosis model for a BIW-welding robot based on a multi-layer belief rule base (BRB) was proposed. This model can effectively integrate monitoring data and expert knowledge to achieve an accurate fault diagnosis and facilitate traceability. First, according to the established fault tree, a fault mechanism was determined. Second, based on the multi-layer relationship of a fault tree, we established a multi-layer BRB model. Meanwhile, in order to improve the accuracy of the model parameters, the projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) algorithm was used to optimize and update the parameters of the fault diagnosis model. Finally, the validity of the proposed model was verified by a simulation experiment for the BIW-welding robot.