Layout Optimization Algorithm Research Articles

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

Retracted: Multiobjective Algorithm for Urban Land Spatial Layout Optimization

Retracted: Multiobjective Algorithm for Urban Land Spatial Layout Optimization

An online surrogate-assisted neighborhood search algorithm based on deep neural network for thermal layout optimization

Thermal layout optimization problems are common in integrated circuit design, where a large number of electronic components are placed on the layout, and a low temperature (i.e., high efficiency) is achieved by optimizing the positions of the electronic components. The operating temperature value of the layout is obtained by measuring the temperature field from the expensive simulation. Based on this, the thermal layout optimization problem can be viewed as an expensive combinatorial optimization problem. In order to reduce the evaluation cost, surrogate models have been widely used to replace the expensive simulations in the optimization process. However, facing the discrete decision space in thermal layout problems, generic surrogate models have large prediction errors, leading to a wrong guidance of the optimization direction. In this work, the layout scheme and its temperature field are represented by images whose relation can be well approximated by a deep neural network. Therefore, we propose an online deep surrogate-assisted optimization algorithm for thermal layout optimization. First, the iterative local search is developed to explore the discrete decision space to generate new layout schemes. Then, we design a deep neural network to build an image-to-image mapping model between the layout and the temperature field as the approximated evaluation. The operating temperature of the layout can be measured by the temperature field predicted by the mapping model. Finally, a segmented fusion model management strategy is proposed to online updates the parameters of the network. The experimental results on three kinds of layout datasets demonstrate the effectiveness of our proposed algorithm, especially when the required computational budget is limited.

Triple-layered chaotic differential evolution algorithm for layout optimization of offshore wave energy converters

Renewable energy sources are progressively assuming a pivotal role in shaping our future, and wave energy stands out as a promising avenue due to its substantial potential and minimal ecological impact. Consequently, extensive research has been directed towards optimizing the layout of wave energy converters (WECs). However, as the number of optimized buoys increases, so does the complexity of calculating hydrodynamic interactions, placing great demands on computing power. Simultaneously, these dynamic interactions can yield either constructive or detrimental outcomes, amplifying the intricacy of layout evaluation. Effectively and promptly determining the optimal buoy arrangement to achieve high energy output efficiency emerges as a pivotal research challenge. We propose a chaos-based differential evolutionary algorithm with a three-layer information structure, including excavation, balancing, and recycling layers, which reasonably adjusts the population structure and makes full use of individual information to optimize the next exploration by combining with chaotic maps. We compared our method with other state-of-the-art intelligent algorithms applied to the problem of wave energy generators and tested it in four real wave scenarios (Perth, Adelaide, Tasmania, and Sydney) using numerical modeling to calculate its energy output. The experimental results show that our improved strategy improves the energy output of the oscillating buoy-type wave energy generator by an average of 101.5%, 93.1%, 23.1%, and 0.7% compared to the mainstream state-of-the-art algorithm for the four scenarios, respectively.

A Large-Scale Sensor Layout Optimization Algorithm for Improving the Accuracy of Inverse Finite Element Method.

The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional strain sensor layout optimization problems of complex antenna truss models. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and gbest selection and particle updating strategies, enhancing the reconstruction accuracy of iFEM for antenna truss structure and algorithm efficiency. The strain sensors optimization deployment on the antenna truss model for different postures is achieved, and the numerical results show that the optimization algorithm IALSCC proposed in this paper can well handle the high-dimensional sensor layout optimization problem.

Research on a random search algorithm for wind turbine layout optimization

Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search.

Layout Optimization Algorithms for the Offshore Wind Farm with Different Densities Using a Full-Field Wake Model

To decrease the power deficit of a wind farm caused by wake effects, the layout optimization is a feasible way for the wind farm design stage. A suitable optimization algorithm can significantly improve the quality and efficiency of the optimization process. For exploring the high-performance algorithms under different layout densities, a comparison is conducted by optimizing the layout of a real offshore wind farm with five algorithms, namely two population-based algorithms and three single-point algorithms. Wake effects are considered by a full-field wake model. A penalty function is proposed for the population-based algorithms to handle the constraint violations. Different iterations and constraints of the layout density are applied in the optimization. The random search has the best optimization results in all the cases and the control of the feasibility check is necessary for this algorithm. More iterations can advance the optimization results. The density constraint greatly affects the computational cost of the random search, which is significantly increased under the strict constraint. Except under the strict constraint, the random search has the best performance of optimization efficiency. A combination of the pattern search and random search is recommended when the strict constraint is applied in the layout optimization.

A low-complexity evolutionary algorithm for wind farm layout optimization

The wind farm layout determines the power generation ability, and its optimization remains challenging in terms of algorithm complexity and performance. Several greedy-derived and evolutionary-derived algorithms have been proposed to solve or alleviate the layout planning problems. However, greedy algorithms are efficient in finding the optimal layout but their exploration ability is poor. In contrast, evolutionary algorithms own better global search ability but require huge computational effort to find the optimal layout. Considering that all wind turbines are deployed in the same flat area (i.e. the coordinates of each wind turbine are of dimension 2, and each dimension of all turbines has the same search range), a low-complexity grey wolf optimization technique using a 2-D encoding mechanism (GWOEM) is proposed in this work. which is low-complexity and accelerates search efficiency. The proposed GWOEM effectively combines the advantages of the greedy algorithms and the evolutionary algorithms, and improves the search efficiency while maintaining accuracy. To validate the performance of the proposed GWOEM, a comparative analysis is performed based on two wind scenarios. Simulation results show that the proposed GWOEM is competitive with other state-of-the-art techniques. Compared with TDA, ADE, ADE-GRNN, and DEEM, the average power output in the two wind scenarios considered herein increases by 44.41%, 34.45%, 34.97%, and 37.06%, respectively. In terms of execution time, GWOEM is shorter than TDA, ADE, and DEEM except for ADEGRNN which increases by 12.18%.

GNN at the Edge: Cost-Efficient Graph Neural Network Processing Over Distributed Edge Servers

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN’s unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost reduction in a fast convergence speed.

Via-Based Redistribution Layer Routing for InFO Packages With Irregular Pad Structures

The integrated fan-out (InFO) wafer-level chip-scale package (WLCSP) is introduced for modern system-in-package designs with larger I/O counts, higher interconnection density, and small form factors. A redistribution layer (RDL) in an InFO package is an extra metal layer for interchip connections, and RDL routing is crucial for achieving desired interchip connections. In a high-density InFO package, multiple RDLs with flexible vias are often adopted. To integrate chips of different technology nodes into one package, irregular pad structures need to be considered; however, no published RDL routing work considers flexible vias or irregular pad structures. This article formulates a new RDL routing problem with unified-assigned pad pairs on a via-based multilayer multichip InFO package with irregular pad structures and presents the first algorithm for this problem. The algorithm consists of a preprocessing stage, three routing stages, and a layout optimization stage. The preprocessing stage analyzes routing resources and potential routing congestion. The first routing stage performs layer assignment based on a weighted maximum planar subset of chords algorithm to route interchip nets concurrently. The second routing stage constructs a 3-D routing graph based on partitioned octagonal tiles to handle the irregular layout structure and applies A*-search to route remaining interchip nets. The third routing stage transforms a routing graph into a network-flow model to perform concurrent routing for chip-to-board nets by applying the minimum-cost maximum-flow algorithm. Finally, we develop an efficient linear-programming-based layout optimization algorithm to find desired solutions. Experimental results show that our router can achieve 100% routablility for all benchmarks under limited RDLs, while the previous state-of-the-art work cannot.

A bi-population clan-based genetic algorithm for heat pipe-constrained component layout optimization

Component Layout Optimization (CLO) is attracting growing attention as electronic equipment rapidly advances toward integration, miniaturization, and high function. Heat pipe-constrained Component Layout Optimization (HCLO) seeks to maximize the thermal performance of electronic equipment by optimizing its component layout, where heat dissipation relies heavily on heat pipes. HCLO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various CLO applications, they do not consider heat pipe-related constraints and therefore underperform on HCLO. In this paper, we propose a Bi-population Clan-based Genetic Algorithm (BCGA) tailored for HCLO. BCGA introduces (i) A bi-population strategy to decompose HCLO into two interrelated sub-problems, which alleviates the challenge of Many-dimensions and Many-constraints; (ii) A clan-based framework inspired by human evolution to handle Many-optima, under which improved GA operators are also designed. The performance of BCGA is evaluated on a suite of HCLO benchmark problems of varying complexity. Compared with other algorithms recently proposed for CLO and HCLO, BCGA can deliver much more satisfying layout solutions within only half of the computation time.

JayaX Algorithm for Simultaneous Layout and Size Optimization of Grillages

JayaX Algorithm for Simultaneous Layout and Size Optimization of Grillages

Design and Realization of Seeding Quality Monitoring System for Air-Suction Vibrating Disc Type Seed Meter

To improve the seeding qualification rate and stability of the air-suction vibrating disc type seed meter on the rice seedling raising line, in this paper, an improved wireless sensor network node layout optimization algorithm was proposed, and the operation monitoring system of the seed meter was designed using the Internet of Things and configuration software. In the system, the upper computer software adopted the Kingview software, the lower computer took the STM32F429IGT and CC2530 as the core controllers, and ZigBee was selected for data transmission to build the wireless sensor network. The acquisition of field status information and the sending of control instructions were realized through the sensor nodes constructed by the CC2530 core controller. The data was sent to the coordinator node in real-time through the wireless sensor network. The coordinator node realized the bidirectional transmission of data with Kingview and the upper computer control instructions forwarding using the ASCII protocol The host computer monitoring and management software was developed based on configuration software to realize real-time data monitoring, access database storage, fault alarm, control command sending and other functions. The experimental results showed that the detection accuracy of the system for the seeding amount and missed seeding amount was 94.3% and 95.6%, respectively, which could realize the monitoring of the primary working status of the seed meter. The system realized effective data transmission and data remote wireless transmission function, which provided sufficient theoretical and data support for the performance optimization of the seed meter, and laid a good foundation for the visualization and intelligence of information data.

Hydrodynamic interactions among wave energy converter array and a hierarchical genetic algorithm for layout optimization

This paper focuses on the layout optimization and hydrodynamic investigation of wave energy converter (WEC) array. For hydrodynamic analysis, each buoy is modeled as a truncated floating cylinder that can move with five degrees of freedom. The hydrodynamic response of such an array is analyzed by a semi-analytical method. Then, the power extraction by the array is used as the objective function for layout optimization. To improve efficiency, especially for an array with a large number of WECs, a novel hierarchical optimization method based on matrix coded genetic algorithm is proposed. Within this method, a large-scale array is firstly divided into several levels of small-scale arrays which are easy to handle, then, the genetic algorithm developed in this paper is used to optimize the small array of each level. An example of two-level optimization is given in this paper and compared with the result by conventional optimization routine. Finally, we investigate the influence of introducing other degrees of freedom, besides the heave mode, on the performance of arrays. Results show that as the constraints get weaker in non-heave directions, the effect of other degrees of freedom on the WEC array interaction factor becomes more and more obvious.

Multiobjective Algorithm for Urban Land Spatial Layout Optimization

In order to explore a quantitative and multiobjective optimization method of land use spatial allocation, this paper proposes a multiobjective algorithm for urban land spatial layout optimization. In this paper, the optimal multiobjective particle swarm optimization (MSO) algorithm is used to construct the optimal land use allocation model, and the variation characteristics of the optimized land use allocation scheme in quantity structure and spatial layout are analyzed. The results show that the total running time of the MSO model and the ordinary genetic algorithm spatial optimal allocation model is 8.57 h and 3.31 h, respectively, and the running efficiency of the mosolua model is 61.38% higher than that of the ordinary genetic algorithm spatial optimal allocation model. The configuration was optimized by using the model of land use spatial pattern from the plaque compactness, adjacency, aggregation degree, environmental compatibility, and the overall degree of resource-saving and environmental friendliness than the ordinary genetic algorithm model of optimal configuration results, and the model of overall fitness model compared with the ordinary genetic algorithm improved by 12.57%.

A Surrogate-Assisted Evolutionary Algorithm for Space Component Thermal Layout Optimization

In space engineering, the electronic component layout has a very important impact on the centroid stability and heat dissipation of devices. However, the expensive thermodynamic simulations in the component thermal layout optimization problems bring great challenges to the current optimization algorithms. To reduce the cost, a surrogate-assisted evolutionary algorithm with restart strategy is proposed in this work. The algorithm is consisted of the local search and global search. A restart strategy is designed to make the local search jump out of the local optimum promptly and speed up the population convergence. The proposed algorithm is compared with two state-of-the-art algorithms on the CEC2006, CEC2010, and CEC2017 benchmark problems. The experiment results show that the proposed algorithm has a high convergence speed and excellent ability to find the optimum in the expensive constrained optimization problems under the very limited computation budget. The proposed algorithm is also applied to solve an electronic component layout optimization problem. The final results demonstrate the good performance of the proposed algorithm, which is of great significance to the component layout optimization.

Write‐An‐Animation: High‐level Text‐based Animation Editing with Character‐Scene Interaction

Abstract3D animation production for storytelling requires essential manual processes of virtual scene composition, character creation, and motion editing, etc. Although professional artists can favorably create 3D animations using software, it remains a complex and challenging task for novice users to handle and learn such tools for content creation. In this paper, we present Write‐An‐Animation, a 3D animation system that allows novice users to create, edit, preview, and render animations, all through text editing. Based on the input texts describing virtual scenes and human motions in natural languages, our system first parses the texts as semantic scene graphs, then retrieves 3D object models for virtual scene composition and motion clips for character animation. Character motion is synthesized with the combination of generative locomotions using neural state machine as well as template action motions retrieved from the dataset. Moreover, to make the virtual scene layout compatible with character motion, we propose an iterative scene layout and character motion optimization algorithm that jointly considers character‐object collision and interaction. We demonstrate the effectiveness of our system with customized texts and public film scripts. Experimental results indicate that our system can generate satisfactory animations from texts.

A layout optimization method based on wave wake preprocessing concept for wave-wind hybrid energy farms

Major investment of renewable energy currently focuses on wind and solar, which are commercially mature. However, there is no large commercial application of wave energy, despite more than four decades of continuous development. Previous research has indicated that wave energy could supply a significant portion of world electricity consumption. Therefore, it is critical to incentivize the utilization of wave energy. The hybrid energy farms, combining wave energy with wind energy, have been considered as one of the most viable solutions to promote mature grid integration of wave energy. However, combining wind and wave requires the identification of adequate locations for both resources and development of layout optimization algorithms capable of handling the complexity of wave wakes. Wave wake analysis has been one of the biggest hurdles for the development of recursive wave farm layout optimization algorithms due to the required extremely time consuming computation processes for each wave wake iteration. This research proposes a new approach by preprocessing the wave wakes beforehand the actual execution of the recursive layout optimization algorithm. This proposed preprocessed wave wake model can be integrated with the different optimization algorithms to identify optimal layouts for hybrid wave-wind farms. The new approach was tested in two selected locations in the Gulf of Mexico with over 36 years (1979–2015) of historical meteorological data. It identifies locations capable of sustaining commercially viable levels of wind and wave energy while simultaneously avoiding risk from extreme oceanic conditions that in the past have damaged or destroyed wave energy converters. Although the two locations have different meteorological conditions, the new approach was able to identify layouts with promising results in both locations. Results indicated that the selected locations could produce very good power output with a wave-wind hybrid energy farm, and most wave and wind energy devices generated capacity factor with values higher than commercial threshold limits.

Wind farm layout optimization using pseudo-gradients

Abstract. This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a “pseudo-gradient”, that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points – speed, competitive reduction in wake losses, and flexibility – and weak points – partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.