Ship Stowage Scheduling of Container Terminal by Combinatorial Optimization Algorithm and Strategy

Optimal design of an autonomous underwater helicopter's shape based on combinatorial optimization strategy

A combinatorial algorithm, including dynamic programming and conjugate gradient, was proposed for 4D trajectory global optimization of the lunar probe vertical soft landing. By employing the suboptimum control variable calculated from dynamic programming as the initial control variable of conjugate gradient algorithm, the more precise optimum control variable and flight trajectories were obtained. The lunar probe vertical soft landing trajectory consists of two segments, <em>i.e</em>., the brake and landing segments. The performance index is to minimize fuel consumption of the lunar detector. The 4D trajectory of lunar vertical soft landing is optimized by this combinatorial optimization algorithm. Simulation results showed good accuracy, rapid convergence, and robustness with initial values for this combinatorial algorithm, which promise it to on-line generate a feasible vertical soft landing trajectory on the onboard computer. Meanwhile, the combinatorial algorithm could be extended to other two-point boundary value problems of terminal time uncontrolled type.

This paper first considers population aging as a breakthrough, analyzes the demand for long-term care due to population aging, and classifies the models and characteristics of long-term care. Secondly, the combinatorial optimization algorithm and its classical cases are analyzed, and a care resource allocation system is built based on the solution set performance metrics and multi-objective combinatorial optimization algorithm for an aging population society. In city A, a research experiment examines the elderly population and the allocation of nursing care resources. The results show that the Gini coefficient of the population distribution of nursing institutions is 0.027 and the Gini coefficient of the population distribution of the number of institutional beds is 0.296, both of which are less than 0.3, thus indicating that the overall structure of the current social nursing resource allocation is relatively fair.

In this paper a new approach is developed to optimize the engine and hydraulic element size and their corresponding parameters of a hybrid hydraulic powertrain to given or assumed load/driving cycles. A multi objective optimization algorithm in combination with boundary condition regulation is applied in a loop. In addition, a new mixed optimization algorithm is proposed to overcome the problem of power management optimization by obtaining a better spread of solutions. The main contribution of the paper is the optimal selection of the motor and accumulator size thereby ensuring optimal vehicle dynamic and power consumption properties to different accumulator and engine sizes.

Hyper spectral image are used in various applications such as geological systems, geo sciences and astronomy. These images are acquired using remote sensing. Remote sensing is the process of getting information about an object without making any physical contact with the object. Satellite Images referred as hyper spectral images are the most used images in remote sensing and are of more interest to find out the classification of objects in those images. The classification can give us the important factors like vegetation, buildings, roads and more. Satellite images can be of assistance in supervision of effects due to natural disasters, to recognize mining areas which are hidden from human view, biodiversity examination, rural and urban environment detection for analysis, etc. However, occasionally the Satellite images acquired can be affected by unforeseen distortions, artificial unwanted structures called artifacts that are formed by the tool itself or sometimes due to the diverse pre-processing procedures involved. Optimization algorithms in combination with Image processing methods are used to classify the objects in satellite images for easy perception and analysis. In this paper, various optimization techniques like particle swarm optimization (PSO), DPSO, HSO, and Proposed MFA optimization algorithms are compared to obtain optimal classification of objects in a satellite image.

Parametric modelling tools have allowed architects and engineers to explore complex geometries with relative ease at the early stage of the design process. Building designs are commonly created by authoring an abstract graph representation that generates building geometry in model space. Once the graph is constructed, design exploration can occur by adjusting metric sliders either manually or automatically using optimization algorithms in combination with multi-objective performance criteria. In addition, subjective aspects such as visual and social concerns may be included in the search process. The authors propose that whilst this way of working has many benefits if the building type is already known, the inflexibility of the graph representation and its top-down method of generation are not well suited to the conceptual design stage where the search space is large and constraints and objectives are often poorly defined. In response, this paper suggests possible ways of liberating parametric modelling tools by allowing changes in the graph topology to occur as well as the metric parameters during a building design and optimisation.

MDLRPPD (Multi-depot Location Routing Problem with Pickups and Deliveries)to be studied in this paper is a kin d of problem very close to reality in the optimization of logistics system. In this paper , the complementary restraint relationship between depot selection, transports, p ickups and deliveries is analyzed, the mathematical model with the lowest total cost i n the logistics system is established, and the verification of correctness of mathematical model established through the application of LINGO software under the condition of small-scale data is conducted. Because of its NP-hard property, the pap er designs the taboo-simulated annealing combinatorial optimization algorithm to r equire a solution, and applies C++ language programming to realize this combinatorial optimization algorithm. Through the comparison with optimum solution obtained from the application of LINGO software, the results show that the taboo-sim ulated annealing combinatorial optimization algorithm proposed in this paper prove s to be a rapid and effective approach in solving MDLRPPD.

This article focuses on the use of multiobjective particle swarm optimization algorithm in combination with the thermohydrodynamic governing equations of fluid film (i.e. momentum and energy equations) in developing an efficient design method to optimize hydrodynamic partial pad journal bearings, for the first time. The governing equations are solved by using the central difference technique with a successive over relaxation scheme. In the simulation, the lubricant viscosity is changed with the temperature variation in whole fluid film. In this problem, the bearing power loss, the minimum oil film thickness, and the maximum oil temperature are selected as three objective functions and the radial clearance and length-to-diameter ratio are considered as two important design variables. The results of the objective functions are presented in tabular and Pareto-front curves. Further, the effect of bearing speed, bearing load, and inlet oil temperature on the mentioned objective functions is illustrated.

Geometric and/or aerodynamic wing twist can be used to produce a lift distribution that results in a rolling moment. A decomposed Fourier-series solution to Prandtl’s lifting-line theory is used to develop analytic spanwise antisymmetric twist distributions for roll control that minimize induced drag on wings of arbitrary planform in pure rolling motion. Roll initiation, steady rolling rate, and the transition between the two are each considered. It is shown that if these antisymmetric twist distributions are used, the induced drag is proportional to the square of the rolling moment, and the induced drag during a steady rolling rate is equal to that on the wing at the same lift coefficient with no rolling rate or antisymmetric twist distribution. Results also show that if these antisymmetric twist distributions are used on straight, tapered wings without symmetric twist, any rolling maneuver for which the rolling rate and rolling moment have the same sign will always produce a yawing moment in the opposite direction. Computational results are also included, which were obtained using a gradient-based optimization algorithm in combination with a modern numerical lifting-line algorithm to find the optimum twist solutions. The resulting twist, induced drag, and yawing moment solutions compare favorably with the analytic solutions developed in the text. The solutions presented here can be used to inform the design of morphing aircraft.

A coaxial contra-rotating open rotor belongs to the next generation of aero-engines. It has an efficiency that is about 30% higher than that of a conventional turbojet engine. However, because of the high noise level, the open rotor has not been introduced into the commercial aviation market. Although there have been numerous efforts to reduce its noise level, the need to accurately predict the unsteady and complex flow field around the open rotor makes it difficult to apply the conventional design methodologies. In this paper, we introduce a state-of-the-art design methodology for solving the unsteady flow field problem of the lownoise open rotor design. A harmonic balance method that is an order of magnitude more efficient than the conventional time accurate CFD method is used to predict the aerodynamic performance of the open rotor. With the accurate formulation of the governing equations through the harmonic balance method, a design method that uses a surrogate model is employed to find optimum configuration that minimizes the noise level and total power at a constant thrust level. A noise prediction is made using the Farassat formula, derived from the Ffowcs-Williams-Hawking’s equation. To efficiently search for the optimum configuration, the design optimization is divided into the rotor topology design level and blade planform design level. In a previous study, we investigated the optimum rotor topology parameters such as the blade radii, rotor spacing, and pitch angle of the aft rotor. In this paper, an investigation is conducted to determine the optimum planform variables such as the twist angle, chord length for several design sections, and tip shape control parameters for the aft rotor. A genetic algorithm is used as a multi-objective optimization algorithm in combination with the Kriging surrogate model. Through the planform design for the aft rotor, the noise level and power consumption of the optimum rotor are reduced by 0.6 dB and 6.8% respectively.

Novel structures and superconductivities of calcium–lithium alloys at high pressures: A first-principles study

Compensation for target speckle phase by use of the combination of the adaptive particle swarm optimization algorithm and the array detector method in heterodyne detection

<abstract><p>Recent advancements in network and multimedia technologies have facilitated the distribution and sharing of digital videos over the Internet. These long videos contain very complex contents. Additionally, it is very challenging to use as few frames as possible to cover the video contents without missing too much information. There are at least two ways to describe these complex videos contents with minimal frames: the keyframes extracted from the video or the video summary. The former lays stress on covering the whole video contents as much as possible. The latter emphasizes covering the video contents of interest. As a consequence, keyframes are widely used in many areas such as video segmentation and object tracking. In this paper, we propose a keyframe extraction method based on multiple features via a novel combinatorial optimization algorithm. The key frame extraction is modeled as a combinatorial optimization problem. A fast dynamic programming algorithm based on a forward non-overlapping transfer matrix in polynomial time and a 0-1 integer linear programming algorithm based on an overlapping matrix is proposed to solve our maximization problem. In order to quantitatively evaluate our approach, a long video dataset named 'Animal world' is self-constructed, and the segmentation evaluation criterions are introduced. A good result is achieved on 'Animal world' dataset and a public available Keyframe-Sydney KFSYD dataset <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup>.</p></abstract>

In this paper, a new intrusion detection system (IDS) is presented to deal with distributed denial of service (DDoS) attacks. A combined algorithm based on Harris Hawks Optimization (HHO) and Dragonfly Algorithm (DA) is proposed to select relevant features and eliminate irrelevant and redundant features from the NSL-KDD dataset. The extracted features are presented to a multilayer perceptron (MLP) neural network. This network (as a classifier) divides the network traffic into two classes, normal and attack categories. Performance of the proposed model is evaluated with two standard and widely-used datasets in the field of intrusion detection: NSL-KDD and UNSW-NB15. The results of the simulations clearly show the superiority of the proposed method compared to the previous methods in terms of critical evaluation criteria such as accuracy, precision, recall, and F-Measure. Specifically, the proposed method exhibited improvements of 96.9%, 97.6%, 96%, and 96.8% in these metrics, respectively (compared to the baseline method). The main reason for these improvements is the ability of the combined algorithm to intelligently select the optimal features and reduce the dimensions of the data. This careful selection of features allows the MLP neural network to focus on critical information, increasing the classification accuracy and ultimately improving the performance of the intrusion detection system. This research showed that combining optimization algorithms and machine learning works well. So, it is effective for tackling DDoS attacks. It can lead to better intrusion detection systems. These systems will be more efficient and accurate.

Label cost optimization proposes a new improvement in label cost function, improving existing moves of α-expansion algorithm and introducing some new moves for this algorithm. In order to study the performance comparison, different metrics of energy minimization has been considered. An appropriate comparison has been drawn among proposed technique i.e. fast approximation algorithm and previous well known techniques. The objective is to effectively optimize energies so that satisfactory image segmentation can be obtained (represented with different labels respective to different objects). New combinatorial optimization algorithm have been proposed which shows promising experimental results with the new moves, which we believe could be used in any context where α -expansions are currently employed.