Topology Optimization for a Large-Scale Truss Bridge Using a Hybrid Metaheuristic Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

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A Matheuristic Approach Combining Local Search and Mathematical Programming

Scientific Programming ◽

10.1155/2016/1506084 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Carolina Lagos ◽

Guillermo Guerrero ◽

Enrique Cabrera ◽

Stefanie Niklander ◽

Franklin Johnson ◽

...

Keyword(s):

Mathematical Programming ◽

Local Search ◽

Large Scale ◽

Search Algorithm ◽

Facility Location Problem ◽

Specific Information ◽

Local Search Algorithm ◽

Capacitated Facility Location Problem ◽

Capacitated Facility Location ◽

Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

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Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

Energy & Environment ◽

10.1177/0958305x211014875 ◽

2021 ◽

pp. 0958305X2110148

Author(s):

Mojtaba Shivaie ◽

Mohammad Kiani-Moghaddam ◽

Philip D Weinsier

Keyword(s):

Electricity Market ◽

Large Scale ◽

Search Algorithm ◽

Power Grid ◽

Mathematical Formulation ◽

Test System ◽

Bidding Strategy ◽

Level Problem ◽

Optimal Bidding ◽

Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

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A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽

10.3390/en12183586 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3586 ◽

Cited By ~ 1

Author(s):

Sizhou Sun ◽

Jingqi Fu ◽

Ang Li

Keyword(s):

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Search Algorithm ◽

Interpolation Method ◽

Least Square ◽

Support Vector ◽

Two Stage ◽

Wind Power Forecasting ◽

Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

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Structural topology optimization of bridge girders in cable supported bridges

IABSE Conference, Kuala Lumpur 2018: Engineering the Developing World ◽

10.2749/kualalumpur.2018.0975 ◽

2018 ◽

Author(s):

Mads Baandrup ◽

Ole Sigmund ◽

Niels Aage

Keyword(s):

Topology Optimization ◽

Large Scale ◽

Design Method ◽

Optimization Method ◽

Bridge Girders ◽

Structural Topology ◽

Box Girders ◽

Box Girder ◽

Fine Mesh ◽

Further Development

This work applies a ultra large scale topology optimization method to study the optimal structure of bridge girders in cable supported bridges.The current classic orthotropic box girder designs are limited in further development and optimiza tion, and suffer from substantial fatigue issues. A great disadvantage of the orthotropic girder is the loads being carried one direction at a time, thus creating stress hot spots and fatigue problems. Hence, a new design concept has the potential to solve many of the limitations in the current state of-the-art.We present a design method based on ultra large scale topology optimization. The highly detailed structures and fine mesh-discretization permitted by ultra large scale topology optimization reveal new design features and previously unseen eff ects. The results demonstrate the potential of gener ating completely different design solutions for bridge girders in cable supported bridges, which dif fer significantly from the classic orthotropic box girders.The overall goal of the presented work is to identify new and innovative, but at the same time con structible and economically reasonable, solutions tobe implemented into the design of future cable supported bridges.

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Context-aware Adaptive Surgery

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3478073 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-22

Author(s):

Hongli Wang ◽

Bin Guo ◽

Jiaqi Liu ◽

Sicong Liu ◽

Yungang Wu ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Resource Constraints ◽

Search Algorithm ◽

Search Time ◽

State Graph ◽

Context Aware ◽

Optimal Partition ◽

Research Attention ◽

Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

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Exact and Heuristic Methods for Observing Task-Oriented Satellite Cluster Agent Team Formation

Mathematical Problems in Engineering ◽

10.1155/2018/2103625 ◽

2018 ◽

Vol 2018 ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Hao Chen ◽

Shu Yang ◽

Jun Li ◽

Ning Jing

Keyword(s):

Large Scale ◽

Search Algorithm ◽

Low Cost ◽

Team Formation ◽

Satellite Systems ◽

Heuristic Search Algorithm ◽

Earth Observation Satellite ◽

Novel Algorithms ◽

Swarm Intelligence Optimization ◽

Task Oriented

With the development of aerospace science and technology, Earth Observation Satellite cluster which consists of heterogeneous satellites with many kinds of payloads appears gradually. Compared with the traditional satellite systems, satellite cluster has some particular characteristics, such as large-scale, heterogeneous satellite platforms, various payloads, and the capacity of performing all the observation tasks. How to select a subset from satellite cluster to perform all observation tasks effectively with low cost is a new challenge arousing in the field of aerospace resource scheduling. This is the agent team formation problem for observation task-oriented satellite cluster. A mathematical scheduling model is built. Three novel algorithms, i.e., complete search algorithm, heuristic search algorithm, and swarm intelligence optimization algorithm, are proposed to solve the problem in different scales. Finally, some experiments are conducted to validate the effectiveness and practicability of our algorithms.

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A New Solution Seed for Job Shop Scheduling Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.110-116.3899 ◽

2011 ◽

Vol 110-116 ◽

pp. 3899-3905

Author(s):

Parviz Fattahi ◽

Mojdeh Shirazi Manesh ◽

Abdolreza Roshani

Keyword(s):

Tabu Search ◽

Large Scale ◽

Production Management ◽

Job Shop ◽

Job Shop Scheduling ◽

Search Algorithm ◽

Tabu Search Algorithm ◽

Np Hard ◽

Shop Scheduling ◽

Large Scale Problems

Scheduling for job shop is very important in both fields of production management and combinatorial optimization. Since the problem is well known as NP-Hard class, many metaheuristic approaches are developed to solve the medium and large scale problems. One of the main elements of these metaheuristics is the solution seed structure. Solution seed represent the coding structure of real solution. In this paper, a new solution seed for job shop scheduling is presented. This solution seed is compared with a famous solution seed presented for the job shop scheduling. Since the problem is well known as NP-Hard class, a Tabu search algorithm is developed to solve large scale problems. The proposed solution seed are examined using an example and tabu search algorithm.

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