Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

Egg investment strategies adopted by a desertic passerine, the Saxaul Sparrow(Passer ammodendri)

Background:As one of the reproductive strategies adopted by bird species,variation in investment in egg production and its influencing factors are important and well-studied subjects.Intraclutch changes in egg size associated with laying order may reflect a strategy of"brood survival"or"brood reduction"adopted by female birds in different situations.Methods:We conducted field studies on the breeding parameters of the Saxaul Sparrow(Passer ammodendri)in Gansu Province,China from 2010 to 2017,to clarify the factors affecting the egg investment and reproductive performance of this passerine species.Results:Our results revealed significant differences in clutch size,egg size and the fledging rate between the first and second brood of Saxaul Sparrows and suggested that this typical desert species allocates more breeding resources to the more favourable second brood period,leading to greater reproductive output.Female body size presented a positive relationship with egg size,and male body size presented positive relationships with clutch size and hatchability.The females that started their clutches later laid more eggs,and hatchability and the fledging rate also increased with a later laying date in the first brood period.With successive eggs laid within the 5-egg clutches(the most frequent clutch size),egg size increased for the first three eggs and then significantly decreased.Conclusions:Our results indicate that female Saxaul Sparrows increased egg investment because of good quality of paired males and good environmental conditions.The intraclutch variation of egg size suggests that this species inhabiting an arid environment adopts a"brood reduction"strategy.

Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses.Real-world emergency material scheduling(EMS)problems are typically large-scale and possess complex constraints.An evolutionary algorithm(EA)is one of the effective methods for solving EMS problems.However,the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints.To handle the above challenges,we apply the idea of a variable reduction strategy(VRS)to an EMS problem,which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems.Firstly,we define an emergency material allocation and route scheduling model,and a variable neighborhood search and NSGA-II hybrid algorithm(VNS-NSGAII)is designed to solve the model.Secondly,we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints.Furthermore,we integrate VRS with VNS-NSGAII to solve the reduced EMS model.To prove the effectiveness of VRS on VNS-NSAGII,we construct two test cases,where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support situation.Experimental results show that VRS can improve the performance of the standard VNS-NSGAII,enabling better optimization efficiency and a higher-quality solution.

Secondary reduction strategy synthesis of Pt-Co nanoparticle catalysts towards boosting the activity of proton exchange membrane fuel cells

Based on the volcanic relationship between catalytic activity and key adsorption energies,Pt-Co alloy materials have been widely studied as cathode oxygen reduction reaction(ORR)catalysts in proton exchange membrane fuel cells(PEMFCs)due to their higher active surface area and adjustable D-band energy levels compared to Pt/C.However,how to balance the alloying degree and ORR performance of Pt-Co catalyst remains a great challenge.Herein,we first synthesized a well-dispersed Pt/Co/C precursor by using a mild dimethylamine borane(DMAB)as the reducing agent.The precursor was calcined at high temperature under H_(2)/Ar mixed gas by a secondary reduction strategy to obtain an ordered Pt_(3)Co intermetallic compound nanoparticle catalyst with a high degree of alloying.The optimization of elec-tronic structure due to Pt-Co alloying and the strong metal-carrier interaction ensure the high kinetic activity of the cell membrane electrode.Additionally,the high degree of graphitization increases the electrical conductivity during the reaction.As a result,the activity and stability of the catalyst were significantly improved,with a half-wave potential as high as 0.87 V,which decreased by only 20 mV after 10000 potential cycles.Single-cell tests further validate the high intrinsic activity of the ordered Pt_(3)Co catalyst with mass activity up to 0.67 A mg_(pt)^(-1),exceeding the United States Department of Energy(US DOE)standard(0.44 A mg_(pt)^(-1)),and a rated power of 5.93 W mg_(pt)^(-1).

A NON-LINEAR MODEL FOR ESTIMATING THE COST OF ACHIEVING EMISSION REDUCTION TARGETS:THE CASE OF THE U.S.,CHINA AND INDIA

With the world talking about climate change, the United States （U.S.）, China and India have announced their carbon emission reduction targets. For these three countries to achieve their targets, significant questions arise, shch as what will be the annual emission reduction efforts to achieve those targets, how much it would cost and what would be the economic effects. This paper puts the carbon intensity reduction targets of China and India together with the absolute emission reduction target of the U.S. into the same non-linear model to quantitatively study the optimal emission control strategies and associated total cost for achieving those targets by the year 2020, and estimate and compare the minimized total costs of the three countries to reach their targets. Our results show that the total cost for the U.S. to achieve its emission reduction target is greater than those of China and India in terms of absolute amount. However, in terms of proportion of total cost to GDP, China and India＇s ratios are significantly greater than that of the U.S., indicating that for the developing countries such as China and India, the achievement of emission reduction targets needs relatively greater effort.

Variable-fidelity optimization with design space reduction

Advanced engineering systems, like aircraft, are defined by tens or even hundreds of design variables. Building an accurate surrogate model for use in such high-dimensional optimization problems is a difficult task owing to the curse of dimensionality. This paper presents a new algorithm to reduce the size of a design space to a smaller region of interest allowing a more accurate surrogate model to be generated. The framework requires a set of models of different physical or numerical fidelities. The low-fidelity （LF） model provides physics-based approximation of the high-fidelity （HF） model at a fraction of the computational cost. It is also instrumental in identifying the small region of interest in the design space that encloses the high-fidelity optimum. A surrogate model is then constructed to match the low-fidelity model to the high-fidelity model in the identified region of interest. The optimization process is managed by an update strategy to prevent convergence to false optima. The algorithm is applied on mathematical problems and a two-dimen-sional aerodynamic shape optimization problem in a variable-fidelity context. Results obtained are in excellent agreement with high-fidelity results, even with lower-fidelity flow solvers, while showing up to 39% time savings.

Toward cost-effective residential energy reduction and community impacts: A data-based machine learning approach

Many U.S.utilities incentivize residential energy reduction through rebates,often in response to state mandates for energy reduction or from a desire to reduce demand to mitigate the need to grow generating assets.The assumption built into incentive programs is that the least efficient residences will be more likely take advantage of the rebates.This,however,is not always the case.The main goal of this study was to determine the potential for prioritized incentivization,i.e.,prioritizing incentives that deliver the greatest energy savings per invest-ment through an entire community.It uses a data mining approach that leverages known building and energy characteristics for predicting energy consumption of houses that collectively can be considered representative of all residences within an entire community.From this model,it estimates natural gas consumption and savings,and corresponding implementation costs associated with the adoption of the most impactful energy reduction measures.The resulting savings and cost estimates allow us to develop a sequential energy reduction strategy whereby the most economic measures within the whole utility district are addressed.The results show that an energy reduction of 36%can be achieved at a levelized cost of less than$14 per mmBTU($14,780 per MJ),demonstrating the strong potential of this approach.A corresponding Economic Input–Output Analysis captures the cascading community economic impacts of this strategy.The results show that for the roughly 45,000 single-family residences in the studied region,an initial energy efficiency investment of$26M could result in a total cascading multiplier economic impact of$41M and additional economic impacts of$2.2M for the lifetime of the considered energy efficiency measures.

Critical steps in the production of oil from oilseed to reduce heterocyclic aromatic amines

Contamination by the heterocyclic aromatic amines(HAAs)norharman and harman is one of the risks in vegetable oil production,so oil producers strive to decrease their content in the end products.In this study,the effects of production conditions—specifically,two pretreatment methods(roasting and microwaving),four extraction methods(two pressing and two solvent extraction)and adsorption refining(four absorbents)—of five oils(flaxseed,peanut,rapeseed,sesame,and sunflower seed)were evaluated.The results showed that microwaving as a pretreatment produced significantly fewer HAAs than roasting(P<0.05).After two pretreatments,the HAA content of oils was ranked from high to low as follows:sunflower oil,sesame oil,flaxseed oil,rapeseed oil,and peanut oil.Solvent-extracted oils(9.16–316.73μg/kg)had fewer HAAs than pressed oils(26.61–633.93μg/kg).Using adsorbents reduced HAAs in oils from the initial 21.77–484.22 to 0.31–41.58μg/kg,and the removal rate reached 99.28%.This study provides critical HAA reduction strategies for application in the oil production process to obtain safe final products.

一种原位还原策略用于构筑无枝晶锌负极

结合了三维结构和亲锌物种的集流体构筑策略被认为是构建高稳定锌金属负极的有效方法.然而,高昂的成本和复杂的制备工艺阻碍了其实际应用.本文通过在有均匀Cu^(2+)锚定的碳布集流体(ACC-600@Cu^(2+))上沉积锌,合理设计了一种稳定的三维锌金属复合阳极(Zn@ACC-600@Cu^(2+)).在锌成核过程中,Cu^(2+)原位还原为金属Cu,然后随着锌的进一步沉积,碳布表面逐渐形成均匀的亲锌的Cu-Zn合金界面层.密度泛函理论计算和实验观察表明,Cu-Zn合金界面不仅可以作为锌离子的亲锌沉积点,而且可以提高导电率,使电场和锌离子通量均匀化.因此,ACC-600@Cu^(2+)集流体可以实现高的镀锌/剥离可逆性,并在15.8 mV的极化电压下稳定循环410 h以上.作为概念验证,我们组装的Zn@ACC-600@Cu^(2+)‖MnO_(2)全电池具有良好的电池倍率性能,与原始碳布相比,其比容量显著提高至110 mA h g^(-1).本文提出的原位还原策略为三维锌金属复合负极的设计提供了一种简便且低成本的方法,促进了无枝晶和高稳定锌金属电池的发展.