An adaptive ant colony system algorithm for continuous-space optimization problems

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

Research on Optimal Design of Campus Space Based on POE Concept: A Case Study of Anhui Xinhua College

Through the analysis of the overall landscape,revetments and roads and plant landscape,10 evaluation factors were determined.The comprehensive evaluation model for the campus space of Anhui Xinhua University was constructed by analytic hierarchy process(AHP).The results showed that revetment safety,road convenience,plant disease resistance and campus activity space were important factors affecting the spatial form planning of campus.Through the comparative analysis of the collected data,optimization suggestions were put forward to provide a basis for the establishment of“people-oriented”campus open space system.

Horizontal well spacing optimization and gas injection simulation for the ultra-low-permeability Yongjin reservoir

Optimal spacing for vertical wells can be effectively predicted with several published methods,but methods suitable for assessing the proper horizontal well spacing are rare.This work proposes a method for calculating the optimal horizontal well spacing for an ultra-low permeability reservoir e the Yongjin reservoir in the Juggar Basin,northwestern China.The result shows that a spacing of 640m is the most economical for the development of the reservoir.To better develop the reservoir,simulation approaches are used and a new model is built based on the calculated well spacing.Since the reservoir has an ultralow permeability,gas injection is regarded as the preferred enhanced oil recovery(EOR)method.Injection of different gases including carbon dioxide,methane,nitrogen and mixed gas are modelled.The results show that carbon dioxide injection is the most efficient and economical for the development of the reservoir.However,if the reservoir produces enough methane,reinjecting methane is even better than injecting carbon dioxide.

State space optimization of finite state machines from the viewpoint of control theory

Motivated by the inconvenience or even inability to explain the mathematics of the state space optimization of finite state machines(FSMs)in most existing results,we consider the problem by viewing FSMs as logical dynamic systems.Borrowing ideas from the concept of equilibrium points of dynamic systems in control theory,the concepts of t-equivalent states and t-source equivalent states are introduced.Based on the state transition dynamic equations of FSMs proposed in recent years,several mathematical formulations of t-equivalent states and t-source equivalent states are proposed.These can be analogized to the necessary and sufficient conditions of equilibrium points of dynamic systems in control theory and thus give a mathematical explanation of the optimization problem.Using these mathematical formulations,two methods are designed to find all the t-equivalent states and t-source equivalent states of FSMs.Further,two ways of reducing the state space of FSMs are found.These can be implemented without computers but with only pen and paper in a mathematical manner.In addition,an open question is raised which can further improve these methods into unattended ones.Finally,the correctness and effectiveness of the proposed methods are verified by a practical language model.

Optimization Transmission Theory and Technical Pathways that Describe Multiscale Urban Agglomeration Spaces

Urban agglomerations are spatial entities that promote the development of ‘new urbanization' processes within China. In this context, the concept of ‘multiscale urban agglomeration spaces' encompasses three linked levels: macroscale urban agglomerations, mesoscale cities, and microscale urban centers. Applying a series of multidisciplinary integrated research methods drawn from geography, urban planning, and architecture, this paper reveals two intensive utilization laws that can be generalized to apply to multiscale urban agglomeration spaces, top-to-bottom ‘positive transmission' linkage and inside-to-outside ‘negative transmission' movement. This paper also proposes optimization transmission theory and policy decision technical pathways that can be applied to these three urban agglomeration spatial scales. Specific technical pathways of transmission include intensive expansion and simulated decision-making in macroscale urban agglomerations, ecology, production, and living space intensive layout and dynamic decision-making in mesoscale cities, and four cores(i.e., ‘single, ring, axis, and pole core') progressive linkage and intensive optimization decision-making in microscale urban centers. The theory and technical pathways proposed in this paper solve the technical problem of optimization and provide intensive methods that can be applied not only at the individual level but also at multiple scales in urban agglomeration spaces. This study also advances a series of comprehensive technical solutions that can be applied to both compact and smart growth cities as well as to urban agglomerations. Solid theoretical support is provided for the optimization of Chinese land development, urbanization, agricultural development, and ecological security.

Research on Spatial Optimization Level Strategy of Beijing Historical Blocks Based on Tourists’Cognitive Evaluation

Tourists,as the main beneficiary of historical block protection and tourism development,are considered qualified to speak on the matter of the preservation of space quality and regional characteristics.The study established a questionnaire using semantic analysis to investigate and evaluate tourists’ perspective of various spatial elements in Beijing’s three historical blocks.Factor analysis and IPA method was introduced to explore how various spatial elements affected the overall environmental perception of tourists.Based on the relative importance of spatial elements,suggestions for optimizing the space of historical blocks were proposed.The study found that:①The overall evaluation of various spatial elements of the historical block was relatively positive,and only the "environmental atmosphere" was closer to the negative evaluation,indicating that most tourists think the environment was too noisy;②All spatial elements can be classified into three indicators:"visual experience","street form" and "perception of regional characteristics";③Landscape planting and space interface needed to be improved first,followed by ground pavement,traffic organization,and advertisement signboard.The research provides a theoretical foundation for reducing the damage caused by improper tourism development behaviors,further improving the spatial quality of historical blocks,and enhancing tourists’ satisfaction of the historical environment.

Optimization of inter-helix spacing for helical piles in sand

The optimization of the inter-helix spacing is a key issue of the axial bearing capacity of helical piles.In this paper,based on the cavity expansion,an analytical approach considering the small-strain stiffness,strength,compressibility and stress level of sand around the helical pile was proposed to analyze the influence zone of the helices to determine the optimal inter-helix spacing in sand.The calculation results of the proposed method were verified using the centrifuge test data and finite element analysis for helical pile in Congleton HST95 sand.They were also compared with those using the Meyerhof pile foundation theory.The results show that the optimal inter-helix spacing based on Meyerhof pile foundation theory differs significantly from the measurement.The range of the influence zone for the helices in sand calculated by the cavity expansion theory matches with the data from literature.The calculation results with the proposed method are consistent with the range of the optimal spacing ratio inferred in the centrifuge tests.The results based on the two-dimensional(2D)finite element model(FEM)are also basically consistent with the calculated analytical solution.

Generalizations of Several Identities Related to Mercier's

The purpose of this paper is to establish sev eral identities containing Gaussian binomial coefficient. These results generali ze several Mercier's results. Key words:Gaussian binomial coefficient; identity; the functio n A(T,T 2,...,T n)culating eige nvalues of auto-correlation matrix of the physical control force of actuators. T he optimization algorithm calculating the optimal actuator placement is then put forward via the minimization of an energy criterion, which is chosen as the con trol index. Numerical examples show the effectiveness of the proposed method.

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.

Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

Hybrid Meta-Model Based Design Space Differentiation Method for Expensive Problems

In this work,a hybrid meta-model based design space differentiation（HMDSD）method is proposed for practical problems.In the proposed method,an iteratively reduced promising region is constructed using the expensive points,with two different search strategies respectively applied inside and outside the promising region.Besides,the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems.Tested upon a serial of benchmark math functions,the HMDSD method shows great efficiency and search accuracy.On top of that,a practical lightweight design demonstrates its superior performance.

Optimization method to obtain appropriate spacing parameters for crop cultivation

Considering the time-consuming and tedious work of the current methods to control plant layout,which is mostly based on expert experience or field trials,we propose an algorithm to optimize and simulate a planting layout based on a virtual plant model and an optimization algorithm.A functional-structural plant model,which combines the structure and physiological function of plants,is used to construct a planting scene.The planting and row spacing are set as the genetic factors and the chromosomes of the genetic algorithm are encoded with a binary method.The photosynthetic yield of the unit planting area is denoted as the fitness value.By using this method,the intercropping of maize and soybean plants and the sole cropping of rice plants are studied.Experimental results show that the proposed method can obtain a high yield planting plan.

Geometry optimization of a barrel silicon pixelated tracker

We have studied optimization of the design of a barrel-shaped pixelated tracker for given spatial boundaries. The optimization includes choice of number of layers and layer spacing. Focusing on tracking performance only,momentum resolution is chosen as the figure of merit. The layer spacing is studied based on Gluckstern＇s method and a numerical geometry scan of all possible tracker layouts. A formula to give the optimal geometry for curvature measurement is derived in the case of negligible multiple scattering to deal with trajectories of very high momentum particles. The result is validated by a numerical scan method, which could also be implemented with any track fitting algorithm involving material effects, to search for the optimal layer spacing and to determine the total number of layers for the momentum range of interest under the same magnetic field. The geometry optimization of an inner silicon pixel tracker proposed for BESIII is also studied by using a numerical scan and these results are compared with Geant4-based simulations.

STRONG N-DISCOUNT AND FINITE-HORIZON OPTIMALITY FOR CONTINUOUS-TIME MARKOV DECISION PROCESSES

This paper studies the strong n(n =—1,0)-discount and finite horizon criteria for continuoustime Markov decision processes in Polish spaces.The corresponding transition rates are allowed to be unbounded,and the reward rates may have neither upper nor lower bounds.Under mild conditions,the authors prove the existence of strong n(n =—1,0)-discount optimal stationary policies by developing two equivalence relations:One is between the standard expected average reward and strong—1-discount optimality,and the other is between the bias and strong 0-discount optimality.The authors also prove the existence of an optimal policy for a finite horizon control problem by developing an interesting characterization of a canonical triplet.