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In this paper, an ultra-thin absorber with a total thickness of 9.2 mm is designed and verified at the frequency band of 1–5.34 GHz. The absorber is composed of a layer of metasurface, multi-layered magnetic substrate, a layer of fragmented magnetic structure obtained by improved MOEA/D-GO (Multi-Objective Evolutionary Algorithm based on Decomposition combined with Enhanced Genetic Operators), and a copper back plate. The absorber is achieved by two steps. First, we designed and measured an ultra-thin absorber at 0.78–2.04 GHz by adding a layer metasurface onto the top of a basic multi-layer absorber composed of magnetic materials. The fractional bandwidth (FBW) of the absorber is 89.4%, and the electrical thickness is only 0.024 λ 0 at the lowest operating frequency. Second, to broaden the bandwidth, we use an improved MOEA/D-GO to optimize one magnetic layer of the absorber. The working frequency band of the optimized absorber is 1–5.34 GHz, covering L- (1–2 GHz), S- (2–4 GHz), and partial C-bands (4–8 GHz). Furthermore, we modified the structure of the metasurface to make the absorber polarization-independent. The FBW of the final absorber is 136.4%, and the electrical thickness is 0.031 λ 0 at the lowest operating frequency. The prototype of the absorber is measured, and the experimental results agree well with the simulated performance. The results show that the improved MOEA/D-GO can be used to design and optimize sophisticated electromagnetic (EM) structures with the predesigned properties, and the absorber with ultrathin thickness and light weight verified in this paper have great application potentials in EM compatibility, EM shielding, and radar cross section reduction at the low bands of the microwave spectrum. [ABSTRACT FROM AUTHOR]

During the architectural design phase, the decisions made have a crucial impact on developing complex systems. These decisions often limit the performance bounds and steer subsequent research directions. However, the multitude of architectural components and intricate interrelationships cause component combination explosion. Additionally, the diverse requirements of different stakeholders add complexity to determining the weight of indicators, posing challenges to complex system architecture generation and trade-off. This paper proposes a method for exploring the architectural design space in complex systems realisation, which aims to resolve issues related to the formal representation of architectures, selection within the architectural design space, and trade-offs. Firstly, the architectural design space is represented by formulating and mathematically describing a morphological matrix. Then, the construction of the Constraint Satisfaction Problem model aims to achieve a reduction in the scale of the design space. Moreover, through the evolutionary algorithm, the Pareto front is identified. Weight sensitivity analysis and the improved Technique for Order Preference by Similarity to the Ideal Solution method are combined to assist designers in finding a satisfactory architecture from the design space with many combinations. Finally, the proposed method is verified by the design of the launch vehicle's primary and secondary separation system. [ABSTRACT FROM AUTHOR]

The three-deck vibrating screen can separate the material into various particle sizes. There are a few types of researchers on the screening efficiency and processing capacity of multideck vibrating screens. In this work, the discrete element method was used to simulate the screening process of the three-deck vibrating screen under different vibration parameters (frequency, amplitude, and inclination angle) and a prototype experiment was carried out. The effects of vibration parameters on the screening performance were analyzed based on the orthogonal design of experiments. An approximate model, the Kriging model, was used to replace the simulation process for further optimization. Then, an optimization algorithm conducted multiobjective optimization, and three sets of optimization solutions were obtained via a traversal approach. In addition, the three optimized vibration parameters were verified by simulation. There is a guideline for the design of vibration parameters of the multideck vibrating screen. [ABSTRACT FROM AUTHOR]

Interbasin water transfer projects (IBWTPs) have become an important measure to alleviate regional water stresses by artificially regulating water resources between water-rich and water-scarce areas. However, there are many conflicting objectives and complex practical operational constraints that complicate the derivation of water allocation schemes for IBWTPs. Here, we propose a multiobjective optimization methodology for deriving water allocation schemes for IBWTPs, which includes three modules: (1) formulating a multiobjective optimal water allocation problem for IBWTPs based on practical operational constraints; (2) proposing a novel multiobjective cuckoo search (NMOCS) algorithm and combining it with a simulation-optimization approach to solve the water allocation problem; and (3) filtering the Pareto solutions using the analytic hierarchy process (AHP)-entropy method. Based on the multiobjective cuckoo search (MOCS) algorithm, the NMOCS employs four improvement strategies, including a population initialization strategy, flock search strategy, multistrategy external archive maintenance strategy, and adaptive parameters, to improve the convergence property and diversity of solutions. To test the performance of the NMOCS, we considered the MOCS and four widely used multiobjective evolutionary algorithms (MOEAs) as a comparison and employed these six MOEAs to solve 11 multiobjective mathematical benchmark problems as well as the water allocation problems of the Jiangsu Province, China, section of the South-to-North Water Diversion Project under normal, dry, and extremely dry hydrological conditions. The results show that the NMOCS outperformed other MOEAs in handling multiobjective mathematical benchmark problems, especially in ZDT1, ZDT2, ZDT6, DTLZ2, DTLZ4, and DTLZ7. Compared with other MOEAs, the NMOCS did not always capture the highest percentage of the reference water allocation schemes, but it provided more than 18% (greater than one-sixth) of effective water allocation schemes for all hydrologic conditions. Meanwhile, compared with optimal water allocation schemes derived from other MOEAs, the NMOCS effectively improved the operational performance under normal and drought hydrological conditions, especially in the total water pumping and the water withdrawn from the Yangtze River. This research can help to update our understanding of MOEAs, particularly the MOCS, and serve as a reference for better-allocating water resources in IBWTPs. [ABSTRACT FROM AUTHOR]

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both expensive and time-consuming, making it impractical for humans to handle vast amounts of data. Consequently, the need for ATS systems has become evident. These systems encounter challenges such as ensuring comprehensive content coverage, determining the appropriate length of the summary, addressing redundancy, and maintaining coherence in the generated summary. Researchers are actively addressing these challenges by employing Natural Language Processing (NLP) techniques. While traditional methods exist for generating summaries, they often fall short of addressing multiple aspects simultaneously. To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. The performance of this enhanced algorithm is assessed by comparing it with state-of-the-art algorithms using the DUC2002 dataset. Experimental results demonstrate that the proposed algorithm significantly outperforms the compared approaches. [ABSTRACT FROM AUTHOR]

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model's accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts. [ABSTRACT FROM AUTHOR]

Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used. [ABSTRACT FROM AUTHOR]

Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in estimating the average treatment effect on the treated. In practice, misspecification of the PS model leads to deviations from theory and matching quality is ultimately judged by the observed post‐matching balance in baseline covariates. Since covariate balance is the ultimate arbiter of successful matching, we argue for an approach to matching in which the success criterion is explicitly specified and describe an evolutionary algorithm to directly optimize an arbitrary metric of covariate balance. We demonstrate the performance of the proposed method using a simulated dataset of 275,000 patients and 10 matching covariates. We further apply the method to match 250 patients from a recently completed clinical trial to a pool of more than 160,000 patients identified from electronic health records on 101 covariates. In all cases, we find that the proposed method outperforms PS matching as measured by the specified balance criterion. We additionally find that the evolutionary approach can perform comparably to another popular direct optimization technique based on linear integer programming, while having the additional advantage of supporting arbitrary balance metrics. We demonstrate how the chosen balance metric impacts the statistical properties of the resulting matched populations, emphasizing the potential impact of using nonlinear balance functions in constructing an external control arm. We release our implementation of the considered algorithms in Python. [ABSTRACT FROM AUTHOR]

Aggregate blending is a complex process especially when more sizes of aggregates need to be blended. For rough usage or to offer an initial answer, the conventional approaches are adequate. However, to obtain a cost-effective blend that meets specification requirements, more aggregate sizes must be accommodated. Several methods were investigated by various authors to deal with this complex process. Most of the methods are confined to a blending of three or four sizes of aggregates only. In this study, an attempt has been made to assess various computational techniques for aggregate blending to optimize the aggregate gradation area. Aggregates grading requirements specified for Dense Bituminous Macadam Grade-2 in MORT&H 500-10 was chosen as a reference. For this grading specifications, various methods like trial-and-error, mid-point gradation, least-square optimization, Generalised Reduced Gradient (GRG) non-linear, GRG multi start, simplex linear programming, regression analysis, IRC SP:89-2010, quadratic programming, support vector machines-SMoreg, path variate analysis, interval method, R programming were employed, and optimal proportions for aggregate blending were obtained. Curve fitting along with integration was applied to determine the aggregate gradation area under the combined gradation curve and maximum density line. The existence of a gradation curve between upper and lower limits and close to the mid-point line is considered as the first check and the minimum aggregate gradation area is considered as a second check while evaluating different optimal proportions arrived. Least aggregate gradation area which implies the minimum percentage of air voids was obtained for simplex LP (UL and LL) and evolutionary algorithm (Genetic algorithm) methods, i.e., 6.885. But these two methods yielded a filler of zero percentage which is practically not possible, hence simplex LP (MID) satisfies all the criterion and is adopted as the best method for aggregate blending. [ABSTRACT FROM AUTHOR]

In this study, a neural network and a multi-objective genetic algorithm were used to optimize the geometric parameters of segmented thermoelectric generators (TEGs) with trapezoidal legs, including the cold end width of thermoelectric (TE) legs (Wc), the ratios of cold-segmented length to the total lengths of the n- and p-legs (Sn,c and Sp,c), and the width ratios of the TE legs between the hot end and the cold end of the n- and p-legs (Kn and Kp). First, a neural network with high prediction accuracy was trained based on 5000 sets of parameters and the corresponding output power values of the TEGs obtained from finite element simulations. Then, based on the trained neural network, the multi-objective genetic algorithm was applied to optimize the geometric parameters of the segmented TEGs with the objectives of maximizing the output power (P) and minimizing the semiconductor volume (V). The optimal geometric parameters for different semiconductor volumes were obtained, and their variations were analyzed. The results indicated that the optimal Sn,c, Sp,c, Kn, and Kp remained almost unchanged when V increased from 52.8 to 216.2 mm3 for different semiconductor volumes. This work provides practical guidance for the design of segmented TEGs with trapezoidal legs. [ABSTRACT FROM AUTHOR]

Topology optimization is one of the most flexible structural optimization methodologies. However, in exchange for its high level of design freedom, typical topology optimization cannot avoid multimodality, where multiple local optima exist. This study focuses on developing a gradient-free topology optimization framework to avoid being trapped in undesirable local optima. Its core is a data-driven multifidelity topology design (MFTD) method, in which the design candidates generated by solving low-fidelity topology optimization problems are updated through a deep generative model and high-fidelity evaluation. As its key component, the deep generative model compresses the original data into a low-dimensional manifold, i.e., the latent space, and randomly arranges new design candidates over the space. Although the original framework is gradient free, its randomness may lead to convergence variability and premature convergence. Inspired by a popular crossover operation of evolutionary algorithms (EAs), this study merges the data-driven MFTD framework and proposes a new crossover operation called latent crossover. We apply the proposed method to a maximum stress minimization problem in 2D structural mechanics. The results demonstrate that the latent crossover improves convergence stability compared to the original data-driven MFTD method. Furthermore, the optimized designs exhibit performance comparable to or better than that in conventional gradient-based topology optimization using the P-norm measure. [ABSTRACT FROM AUTHOR]

In an increasingly competitive and digital industrial environment, the optimization of structures is a key point not only to reduce costs but also to reduce the consumption of natural resources. To this end, different approaches have emerged throughout history based on the tools available at the time. With the current rise of artificial intelligence and the concept of machine learning, revolutionary ideas are emerging that allow an optimal dimensioning of structures in record time. This work presents the use of variational autoencoders and mixed-variable solvers as a proposal for structural optimization and material selection. It has expanded upon previous research by advancing in three directions: (1) incorporating more material attributes, particularly relevant for environmental considerations; (2) analyzing in more detail aspects of VAEs such as the dimensionality of the latent space; and (3) a two-step hybrid approach to select the optimal candidate: preliminary filtering with VAE and final design via mixed-variable model. Various examples demonstrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

As 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. [ABSTRACT FROM AUTHOR]

The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towards the center of the Pareto front due to inadequate selection forces. The study proposes the utilization of a novel approach known as MOEA/D, which partitions complex multi-objective problems into smaller, more feasible single-objective sub-problems. Each sub-problem may then be addressed using an equal amount of computational resources. The predetermined size of the neighborhood used by MOEA/D may lead to a delay in the algorithm's merging and reduce the effectiveness of the failure. The paper proposes the Adaptive Neighbourhood Adjustment Strategy (ANAS) as a novel approach to improve the efficiency of multi-objective optimisation algorithms in order to tackle this issue. The ANAS algorithm allows for adaptive adjustment of the subproblem neighborhood size, hence enhancing the trade-off between merging and variety. In the following section of the study, a novel feature selection technique called MOGHHNS3/DANA is introduced. This technique utilizes ANAS to expand the potential solutions for a particular subproblem. The approach evaluates the chosen features using the Regulated Extreme Learning Machine (RELM) classifier on sixteen benchmark datasets. The experimental results demonstrate that MOGHHNS3/D-ANA outperforms four commonly employed multi-objective techniques in terms of accuracy, precision, recall, F1 score, coverage, hamming loss, ranking loss, and training time, error. The APBI approach in decomposition-based multiobjective optimization focuses on handling constraints by adjusting penalty parameters to guide the search towards feasible solutions. On the other hand, the ANA approach focuses on dynamically adjusting the neighborhood size or search direction based on the proximity of solutions in the detached space to adapt the search process. The proposed approach achieves convergence by minimizing redundancy, preserving diversity in the decision space, and simultaneously enhancing classification accuracy. [ABSTRACT FROM AUTHOR]

Leg mating units (LMU), consisting of steels and hyperelastic materials, are utilized to protect the structures during offshore platform float-over installation. Initial LMU design usually does not satisfy required performance since it is geometrically adjusted to fit given requirements based on previous design. To meet design criteria, design optimization can be applied to the initial design. However, a direct implementation of an accurate local search algorithm may cause heavy computations with the risk of falling into a local optimum. It can be also challenging to find an accurate optimum with efficient global search algorithms due to the characteristics of LMU problems. A small perturbation in nonlinear material can have a significant impact on LMU performance. Therefore, in this research, to find an optimum LMU design efficiently and accurately, an optimization framework that combines global and local search algorithms is implemented. In the framework, an efficient meta-model of optimal prognosis based evolutionary algorithm is first applied. The optimal design obtained from the first step, premature but near the optimum, is set as a new starting point for the sequential optimization where accurate polynomial based local adaptive response surface method is utilized. The robustness of the proposed framework is shown by the development of an LMU suitable for float-over installation of a 20,000-ton topside over an eight-legged fixed offshore jacket platform. [ABSTRACT FROM AUTHOR]

The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that relies on constructing a pairwise comparison matrix (PCM) based on decision-makers' (DMs) judgments regarding alternatives and criteria. The objective is to obtain weights and ultimately rank and select alternatives. However, in some cases, DMs may not provide judgments due to a lack of experience, knowledge, or reluctance to express opinions on the subject, resulting in missing pairs in the PCM. Existing techniques for filling these missing pairs have inherent limitations. This paper demonstrates the application of the pymissingAHP algorithm, implemented in Python and available at https://bit.ly/3UFdqSZ, to address missing pairs using mono and multiobjective approaches. The pymissingAHP algorithm employs a Genetic Algorithm (GA) featuring a specialized encoding to address this issue. Our approach can handle mono or multiple objective scenarios, which involve minimizing the consistency index and maintaining the ranking of weights as defined by experts. Additionally, the algorithm accommodates fuzzy numbers within the AHP framework (FAHP). PCMs containing numerous missing values may yield multiple solutions and not accurately reflect experts' opinions. Although our approach can solve entirely depleted PCMs, obtaining as many comparisons as possible is recommended to ensure a faithful representation of expert opinions for the decision-makers. The pymissingAHP algorithm provides a significant advantage: the capacity to seek solutions that address single or multiple objectives, utilizing continuous or discrete values, and additionally, the ability to solve FAHP problems. [ABSTRACT FROM AUTHOR]

Reconfigurable hardware, extensively employed in mission-critical digital applications like space and military electronics due to its adaptability, encounters the issue of soft errors, especially in control path elements, which could result in functional failure. Various system-level fault tolerance methodologies exist, and this paper implements a bio-inspired fault tolerance technique called evolvable hardware (EHW). The preferred implementation of the EHW system involves hosting the evolutionary algorithm on the processor alongside the reconfigurable hardware. However, this approach encounters delays in the intercommunication of the evolved circuit between the reconfigurable hardware and the processor. To address this issue, the paper proposes a two-tier architecture to achieve absolute fault mitigation in the controller. In this architecture, Tier-1 involves the digital implementation of the genetic algorithm on the reconfigurable hardware to mitigate errors in the controller, while Tier-2 focuses on mitigating errors occurring in Tier-1. The aim is to establish an absolute and self-resilient controller hardware to mitigate faults. The study simulates faults at the target circuit and genetic module as a proof of concept. The proposed two-tier single event upset (SEU) mitigation is deployed on Microsemi's ProAsic3e FPGA (Field Programmable Gate Array), achieving an average efficiency of 91%. This efficiency is accompanied by ten times lesser resource utilization compared to traditional methodologies and a 30% accelerated speed when compared to hybrid evolvable systems. [ABSTRACT FROM AUTHOR]

This study presents a novel framework of mission planning for actively removing multiple space debris in low Earth orbit. The associated problem is formulated as a multi-objective optimization problem to minimize the total velocity increments while maximizing the Hazard Criticality Index (HCI) of the candidate target debris. The main idea is based on the Random-Key (RK) encoding scheme that effectively converts a continuous search space into a discretized solution space representing the target debris and visiting sequence. With the RK encoding technique, the proposed framework can obtain the optimal solutions for target debris selection for removal, removal sequences, and other design variables for computing orbital transfer costs simultaneously in a systematic way. In order to demonstrate these favorable characteristics, the proposed framework is integrated with four well-known multi-objective Evolutionary Algorithms (EAs) to solve mission planning problems for the active removal of multiple space debris in low Earth orbit. The framework integrated with NSGA-II, compared with other EAs, shows the best performance in terms of optimality and diversity of non-dominated solutions. Moreover, the optimization results indicate that our integrated framework appropriately selects target debris with similar orbital elements and naturally employs the Right Ascension of the Ascending Node (RAAN) drift originating from J2 perturbation to shift the transfer orbit to reduce total velocity increments. The overall results show that the proposed framework is effective in the preliminary mission planning for multiple space debris removal in low Earth orbit. [ABSTRACT FROM AUTHOR]

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users' preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor's preferences has proved to be very effective in the portfolio's composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation. [ABSTRACT FROM AUTHOR]

The sensitivity of forecast-informed reservoir operating policies to forecast attributes (lead-time and skill) in many-objective water systems has been well-established. However, the viability of forecast-informed operations as a climate change adaptation strategy remains underexplored, especially in many-objective systems with complex trade-offs across interests. Little is known about the relationships between forecast attribute and policy robustness under deep uncertainty in future conditions and the relationships between forecast-informed performance and future hydrologic state. This study explores the sensitivity of forecast-informed policy robustness to forecast lead-time and skill in the outflow management plan of the Lake Ontario basin. We create water supply forecasts at four different subseasonal-to-annual lead-times and two levels of skill and further employ a many-objective evolutionary algorithm to discover policies tailored for each forecast case, historical supply conditions, and six objectives. We also leverage a partnership with decision-makers to identify a subset of candidate policies, which are reevaluated under a large set of plausible hydrologic conditions that reflect stationary and nonstationary climates. Scenario discovery techniques are used to map attributes of future hydrology to forecast-informed policy performance. Results show policy robustness is directly related to forecast lead-time, where policies conditioned on 12-month forecasts were more robust under future hydrology. Policies tailored for noisier long-lead forecasts were more robust under a wide range of plausible futures compared with policies trained to perfect forecasts, which highlights the potential to overfit control policies to historical information, even for a forecast-informed policy with perfect foresight. The relationship between performance and the hydrologic regime is dependent on the complexity of the interactions between control decisions and objectives. A threshold of objective performance as a function of supply conditions can support adaptive management of the system. However, more complex interactions make it difficult to identify simple hydrologic indicators that can serve as triggers for dynamic management. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a strengthened RIME algorithm to tackle continuous optimization problems. RIME is a newly proposed physical-based evolutionary algorithm (EA) inspired by the soft and hard rime growth process of rime-ice, which has a powerful exploitation ability. But in complex optimization problems, RIME will easily trap into local optima and the optimization will become stagnation. Noticing this issue, we introduce three techniques to the original RIME: (1) Latin hypercube sampling replaces the random generator as the initialization strategy, (2) modified hard rime search strategy, and (3) embedded distance-based selection mechanism. We evaluate our proposed SRIME in 10-D, 30-D, 50-D, and 100-D CEC2020 benchmark functions and eight real-world engineering optimization problems with nine state-of-the-art EAs. Experimental and statistical results show that the introduction of three techniques can significantly accelerate the optimization of the RIME algorithm, and SRIME is a competitive optimization technique in real-world applications. Ablation experiments are also provided to analyze our proposed three techniques independently, and the embedded distance-based selection contributes most to the improvement of SRIME. The source code of SRIME can be found in https://github.com/RuiZhong961230/SRIME. [ABSTRACT FROM AUTHOR]

It is crucial to optimize energy consumption in buildings while considering thermal comfort. The first step here involved an EnergyPlus simulation on a trade center building located in Tehran, Bandar Abbas, and Tabriz, Iran. A multi-objective optimization was then performed based on non-dominated sorting genetic algorithm II (NSGA-II) in jEPlus + EA to establish the building in the selected city where would benefit the most from implementing the radiant ceiling cooling system. Efforts were undertaken to choose environmentally-friendly materials. The final solution by Pareto charts resulted in a 52% reduction in energy consumption, a 37.3% decrease in cooling load, and a 17.4% improvement in comfort hours compared to the original design. Annual emission of greenhouse gas reduced as 167.67 tone of CO2 equivalent emission, 25.77 ton of CH4, and 0.2 ton of NO2. The mentioned algorithm was conducted for the first time on a trade center, including a DOAS system and radiant ceiling cooling system. Simultaneously, the environmental-friendly materials were dealt with. The procedure holds significant relevance for the design and optimization of buildings in Iran, especially wherever the climate is hot and humid. This approach offers advantages to the environment by reducing the impact on energy resources and utilizing environmentally-friendly materials. [ABSTRACT FROM AUTHOR]

Numerous studies revealed optimization techniques' applicability in minimizing the costs of reinforced concrete buildings. However, the existing literature has narrowly focused on optimizing buildings with a single function, such as residential or office buildings, hindering the generalization of the results. This paper aims to bridge the gap between optimization and structural engineering by obtaining the minimum-cost design of flat slab buildings with different intended functions. In this context, the optimal designs of 120 alternatives were obtained, considering various spans (4–8 m), live loads (2–10 kPa), and concrete compressive strength (25–40 MPa). The optimization was executed using the evolutionary algorithm provided in Microsoft Excel's Solver tool. The optimization model permits the utilization of drop panels to resist punching stresses developed from the slab-column interaction. The objective function is the cost of materials and labor involved in constructing floors and columns. The decision variables are the floor dimensions and column configurations in dimensions and reinforcement. The structural constraints were applied per the Egyptian design code (ECP203-2020). Eventually, guidelines were developed to help the designers choose the economic floor system and quantities of materials based on the building's intended function. [ABSTRACT FROM AUTHOR]

Unfair competition on internet platforms (UCIP) has become a critical issue restricting the platform economy's healthy development. This paper applies evolutionary game theory to study how to utilize multiple subjects' synergy to supervise UCIP effectively. First, the "multi-agent co-governance" mode of UCIP is constructed based on the traditional "unitary supervision" mode. Second, the government and internet platform evolutionary game models are built under two supervision modes. Finally, MATLAB is used to simulate and analyze the evolutionary stage and parameter sensitivity. In addition, we match the model's evolutionary stage with China's supervisory process. The results show that (1) the Chinese government's supervision of UCIP is in the transitional stage from "campaign-style" to "normalization." (2) Moderate government supervision intensity is essential to guide the game system to evolve toward the ideal state. If the supervision intensity is too high, it will inhibit the enthusiasm for supervision. If the supervision intensity is too low, it cannot form an effective deterrent to the internet platforms. (3) When the participation of industry associations and platform users is low, it can only slow down the evolutionary speed of the game system's convergence to the unfavorable state. Nevertheless, it cannot reverse the evolutionary result. (4) Maintaining the participation level of industry associations and platform users above a specific threshold value while increasing punishment intensity will promote the transition of government supervision from the "campaign-style" to the "normalization" stage. This paper provides ideas and references for the Chinese government to design a supervision mechanism for UCIP. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing battery fractional order model is not suitable for the auxiliary power battery of hydrogen fuel cell heavy-duty trucks, considering the characteristics of multiple influencing factors and nonlinearity in actual driving processes, a fractional order model of auxiliary power batteries is proposed based on the hydrogen fuel cell heavy-duty trucks model and the batteries' fractional order model. Then, to solve the charge state estimation error increases due to the high frequency and large rate charging and discharging, H ∞ filter is introduced into the adaptive unscented Kalman filter to optimize the charge state estimation in the model. Additionally, the model's parameters are identified using the evolutionary algorithm and the recursive least squares technique based on the forgetting factor. Finally, the model is verified by dynamic conditions. The results show that compared with the auxiliary power batteries' fractional order model, the optimized model reduces the MAE by 0.08% and the RMSE by 0.22% when estimating voltage, and reduces the MAE by 0.25% and the RMSE by 2.66% when estimating SOC. Therefore, the optimized fractional order model of auxiliary power battery has higher accuracy and applicability. • A S-HFOM of the hydrogen fuel cell hybrid systems heavy-duty trucks is established. • H ∞ filter is applied to AUKF to reduce the battery model errors. • The parameters of the S-HFOM are identified using the GA and the FFRLS. • The applicability and accuracy of the S-HFOM are verified under dynamic conditions. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms (MOEA) have attracted much attention because of their good global exploration ability; however, their local search ability near the optimal value is weak, and for large-scale decision-variable optimization problems the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient optimization algorithms can overcome these difficulties well, but gradient search methods are difficult to apply to multi-objective optimization problems (MOPs). To this end, this paper introduces a stochastic weighting function based on the weighted average gradient and proposes two multi-objective stochastic gradient operators. Further, two efficient evolutionary algorithms, MOGBA and HMOEA, are developed. Their local search capability has been greatly enhanced while retaining the good global exploration capability by using different offspring update strategies for different subpopulations. Numerical experiments show that HMOEA has excellent capture ability for various Pareto formations, and it can easily solve multi-objective optimization problems with many objectives, which improves the efficiency by a factor of 5–10 compared with typical multi-objective evolutionary algorithms. HMOEA is further applied to the multi-objective aerodynamic optimization design of the RAE2822 airfoil and the ideal Pareto front is obtained, which indicates that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design. [ABSTRACT FROM AUTHOR]

The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies. [ABSTRACT FROM AUTHOR]

This paper presents an optimization tool for jacket structures to support Offshore Wind Turbines (OWTs). The tool incorporates several combinations of optimization algorithms and constraint-handling techniques (CHTs): Genetic Algorithm; Differential Evolution (DE); Tournament Selection Method; Multiple Constraint Ranking (MCR); Adaptive Penalty Method, and Helper-and-Equivalent Optimization. The objective function regards the minimization of the jacket weight; the design variables are the diameter and thickness of the tubular members. The constraints are related to natural frequencies and Ultimate Limit State criteria. The candidate solutions are evaluated by full nonlinear time-domain Finite Element coupled analyses. To assess the optimization algorithms and CHTs, a case study is presented for the standardized OWT/jacket structure from the Offshore Code Comparison Collaboration Continuation project. First, a numerical model is built and validated, in terms of masses, natural frequencies, and vibration modes; then, this model is employed to run the optimization tool for all combinations of optimization algorithms and CHTs. The results indicate that, while all methods lead to feasible optimal solutions that comply with the constraints and present considerable weight reductions, the best performer is the combination of the DE algorithm with the MCR constraint-handling technique. [ABSTRACT FROM AUTHOR]

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model's efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it's crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. [ABSTRACT FROM AUTHOR]

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry's pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer's cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context. [ABSTRACT FROM AUTHOR]

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. [ABSTRACT FROM AUTHOR]

Kansei Engineering (KE) is a product design method that aims to develop products to meet users' emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user's multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user's multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design. [ABSTRACT FROM AUTHOR]

This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP. [ABSTRACT FROM AUTHOR]

Gait analysis is a widely used technique for passive human identification and tracking, with potential applications in security and surveillance systems. However, existing gait recognition methods face challenges in handling changing angles and uncertain features. In this paper, we propose a novel gait recognition approach that leverages real-time spatio-temporal gait features, including step length, gait cycle, height, cadence, swing ratio, and foot length. We apply the Extreme Learning Machines (ELM) algorithm for classification, which has been shown to be effective in various applications due to its fast-learning speed and good generalization performance. To further enhance the recognition rate, we introduce an evolutionary BAT-optimized ELM algorithm that addresses the instability issue in ELM. The proposed BAT-ELM algorithm can optimize the hidden nodes and weights of ELM, which leads to improved efficiency in recognizing gait from multiple view angles ranging from 0° to 180°. Our comprehensive analysis of the proposed approach indicates that it outperforms other reported algorithms in terms of recognition rate and efficiency. Our work demonstrates the effectiveness of combining real-time spatio-temporal gait features with the BAT-ELM algorithm for gait recognition. The proposed approach has potential applications in various fields, including security and surveillance systems, healthcare, and robotics. Our findings highlight the importance of leveraging evolutionary algorithms to optimize machine learning models and achieve better performance in complex recognition tasks. [ABSTRACT FROM AUTHOR]

The inherently low signal-to-noise ratio of NMR and MRI is now being addressed by hyperpolarization methods. For example, iridium-based catalysts that reversibly bind both parahydrogen and ligands in solution can hyperpolarize protons (SABRE) or heteronuclei (X-SABRE) on a wide variety of ligands, using a complex interplay of spin dynamics and chemical exchange processes, with common signal enhancements between 103 and 104 . This does not approach obvious theoretical limits, and further enhancement would be valuable in many applications (such as imaging mM concentration species in vivo). Most SABRE/X-SABRE implementations require far lower fields (μT-mT) than standard magnetic resonance (>1T), and this gives an additional degree of freedom: the ability to fully modulate fields in three dimensions. However, this has been underexplored because the standard simplifying theoretical assumptions in magnetic resonance need to be revisited. Here, we take a different approach, an evolutionary strategy algorithm for numerical optimization, multi-axis computer-aided heteronuclear transfer enhancement for SABRE (MACHETE-SABRE). We find nonintuitive but highly efficient multiaxial pulse sequences which experimentally can produce a sevenfold improvement in polarization over continuous excitation. This approach optimizes polarization differently than traditional methods, thus gaining extra efficiency. [ABSTRACT FROM AUTHOR]

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis. [ABSTRACT FROM AUTHOR]

This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous behaviors. The target problems are defined as evolutionary multi-objective problems aiming to evolve near optimal neuro-fuzzy controllers that can operate in a variety of scenarios. To enhance the evolution, sequential transfer optimization is considered, where each of the source problems is defined and solved as a bi-objective problem. The performed experimental study demonstrates the ability of the proposed search approach to find neuro-fuzzy controllers that produce the required motion behaviors when operating in various environments with different motion difficulties. Moreover, the results of this study substantiate the hypothesis that solutions with performances near the edges of the obtained approximated bi-objective Pareto fronts of the source problems provide better transferability as compared with those that are associated with performances near the center of the obtained fronts. [ABSTRACT FROM AUTHOR]

Solving a multiple-criteria optimization problem with severe constraints remains a significant issue in multi-objective evolutionary algorithms. The problem primarily stems from the need for a suitable constraint handling technique. One potential approach is balancing the search in feasible and infeasible regions to find the Pareto front efficiently. The justification for such a strategy is that the infeasible region also provides valuable information, especially in problems with a small percentage of feasibility areas. To that end, this paper investigates the potential of the infeasibility-driven principle based on multiple constraint ranking-based techniques to solve a multi-objective problem with a small feasibility ratio. By analyzing the results from intensive experiments on a set of test problems, including the realistic multi-objective car structure design and actuator design problem, it is shown that there is a significant improvement gained in terms of convergence by utilizing the generalized version of the multiple constraint ranking techniques. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate the optimal control of nonlinear fractional order chaotic models of diabetes mellitus, human immunodeficiency virus, migraine and Parkinson's diseases using genetic algorithms and particle swarm optimization. Mathematical chaotic models of nonlinear fractional order type of the above diseases were presented. Then optimal control for each of the models and numerical simulation was done using genetic algorithm and particle swarm optimization algorithm. The results of the genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method is also very successful and the results are very close to the genetic algorithm method. Very low values of MSE and RMSE errors indicate that the simulation is effective and efficient. Also, Lie symmetry was calculated for the proposed models and the results were presented. [ABSTRACT FROM AUTHOR]

DNA computing is one of the more popular computational methods currently studied, but the requirements for nucleic acid molecules in DNA sequences are high, and it is an important challenge to design reasonable and high-quality DNA sequences while satisfying various constraints. Evolutionary algorithms have good applications in DNA sequence optimization problems, but they still have some limitations. To this end, this paper proposes a JAYA algorithm based on normal clouds, referred to as IJAYA, which uses a combinatorial learning approach to update the optimal and worst positions, which is used to manipulate the subsequent merit search means, and then enhances the local search ability of individuals through the normal cloud model, and finally rejects the worst solutions through a harmony search algorithm to find more reasonable and high-quality solutions. The validity of IJAYA is verified in six benchmark functions, in comparison with multiple variants of JAYA and two statistical tests. In the DNA sequence design optimization problem, the average DNA metrics optimized by IJAYA are: 0 (Continuity), 0 (Hairpin), 59.43 (H-measure), 46.57 (Similarity) and 63.79 (Similarity). The feasibility and practicality of IJAYA was verified by comparing it with the solution algorithms proposed in recent years and ablation experiments. [ABSTRACT FROM AUTHOR]

Filter pruning of convolutional neural networks (CNNs) is a common technique to effectively reduce the memory footprint, the number of arithmetic operations, and, consequently, inference time. Recent pruning approaches also consider the targeted device (i.e., graphics processing units) for CNN deployment to reduce the actual inference time. However, simple metrics, such as the ℓ 1 -norm, are used for deciding which filters to prune. In this work, we propose a hardware-aware technique to explore the vast multi-objective design space of possible filter pruning configurations. Our approach incorporates not only the targeted device but also techniques from explainable artificial intelligence for ranking and deciding which filters to prune. For each layer, the number of filters to be pruned is optimized with the objective of minimizing the inference time and the error rate of the CNN. Experimental results show that our approach can speed up inference time by 1.40× and 1.30× for VGG-16 on the CIFAR-10 dataset and ResNet-18 on the ILSVRC-2012 dataset, respectively, compared to the state-of-the-art ABCPruner. [ABSTRACT FROM AUTHOR]

The simulation studies performed on walking quadruped robot, with 8 link coplanar Desai's walking leg mechanism, is conducted using SAM 6.1 software along with the kinematic analysis and motion analysis of a quadruped robot performed in SOLIDWORKS software. The optimised link lengths of each leg in Quadruped robot are obtained based on motion along a straight path and a staircase. The foot profile of optimised model is compared with Desai's, Theos Jansen's, and Joe Klann's walking leg mechanisms. [ABSTRACT FROM AUTHOR]

Recent optical flow estimators usually employ deep models designed for image classification as the encoders for feature extraction and matching. However, those encoders developed for image classification may be sub-optimal for flow estimation. In contrast, the decoder design of optical flow estimators often requires meticulous design for flow estimation. The disconnect between the encoder and decoder could negatively affect optical flow estimation. To address this issue, we propose a neural architecture search method, FlowNAS, to automatically find the more suitable and stronger encoder architecture for existing flow decoders. We first design a suitable search space, including various convolutional operators, and construct a weight-sharing super-network for efficiently evaluating the candidate architectures. To better train the super-network, we present a Feature Alignment Distillation module that utilizes a well-trained flow estimator to guide the training of the super-network. Finally, a resource-constrained evolutionary algorithm is exploited to determine an optimal architecture (i.e., sub-network). Experimental results show that FlowNAS can be easily incorporated into existing flow estimators and achieves state-of-the-art performance with the trade-off between accuracy and efficiency. Furthermore, the encoder architecture discovered by FlowNAS with the weights inherited from the super-network achieves 4.67% F1-all error on KITTI, an 8.4% reduction of RAFT baseline, surpassing state-of-the-art handcrafted GMA and AGFlow models, while reducing the model complexity and latency. The source code and trained models will be released at https://github.com/VDIGPKU/FlowNAS. [ABSTRACT FROM AUTHOR]

Geometry optimization aims to maximize heat transfer rate and minimize pressure drop attending to structural and fabrication constraints. The present work carried out the first optimization study of compact heat exchangers produced by selective laser melting (SLM) with circular channels. No optimization study investigated circular mini channels since most focus on semi-circular channels of printed circuit heat exchangers. Besides, since samples produced by selective laser melting present higher yield strength, it was possible to investigate a higher range of configurations. Analytical models of heat transfer and pressure drop, with structural analysis in finite element model were used in the optimization study. The analysis was conducted using genetic algorithms (NSGA-II) based on evolutionary and dominance concepts to evaluate different configurations. The results showed a strong relationship with the admissible stress limit, so a new study, using the properties SLM samples, was performed. Decision variables' behavior was investigated among all the optimum solutions, besides stress constraint and flow type (cross and counter-flow), resulting in different optimal solutions of Pareto curves. The optimization provided heat transfer and pressure drop ratio from 1.2 kW/Pa to 12.5 kW/Pa. The optimized arrangements were compared with heat exchangers from the literature, demonstrating a 19% improvement in thermal performance and an 85% reduction in pressure drop. [ABSTRACT FROM AUTHOR]

The survival of an animal depends on its success as a forager, and understanding the adaptations that result in successful foraging strategies is an enduring endeavour of behavioral ecology. Random walks are one of the primary mathematical descriptions of foraging behavior. Power law distributions are often used to model random walks, as they can characterize a wide range of behaviors, including Lévy walks. Empirical evidence indicates the prevalence and efficiency of Lévy walks as a foraging strategy, and theoretical work suggests an evolutionary origin. However, previous evolutionary models have assumed a priori that move lengths are drawn from a power law or other families of distributions. Here, we remove this restriction with a model that allows for the evolution of any distribution. Instead of Lévy walks, our model unfailingly results in the evolution of intermittent search, a random walk composed of two disjoint modes—frequent localized walks and infrequent extensive moves—that consistently outcompeted Lévy walks. We also demonstrate that foraging using intermittent search may resemble a Lévy walk because of interactions with the resources within an environment. These extrinsically generated Lévy-like walks belie an underlying behavior and may explain the prevalence of Lévy walks reported in the literature. [ABSTRACT FROM AUTHOR]

For expensive problems, as the number of objectives and the number of decision variables increase, the number of non-dominated solutions in the population increases dramatically, and the population does not have enough convergence pressure to converge to the true pareto frontier. A Kriging-assisted Multi-stage Evolutionary Algorithm (K-MSEA) is proposed to enhance the exploratory capacity of populations in expensive many-objective optimization problems. A multi-stage strategy is used in K-MSEA. Mating selection and environmental selection of populations are divided into three stages based on convergence and diversity indices. Targeted selection of individuals should be conducted at each stage of population development. The population independently chooses the appropriate stage in accordance with the current demands of convergence and diversity. Differential evolutionary algorithm is used to improve the search ability of the population. To guide the population in the correct direction, K-MSEA employs an elite retention strategy in environmental selection. The population utilizes individual renewal, where underperforming parents are replaced by better-performing offspring. The surrogate model is then updated with the uncertainty information supplied by the Kriging model. Finally, K-MSEA is benchmarked against five other algorithms in experiments on two benchmark problems and two realistic expensive problems. Mathematical analysis demonstrates that K-MSEA is more competitive than others. [ABSTRACT FROM AUTHOR]