Simulated Annealing-based Algorithm Research Articles

Autonomous On-Demand Deployment for UAV Assisted Wireless Networks

Unmanned aerial vehicle (UAV) assisted wireless network has been recognized as an effective technology to facilitate the formation of a super flexible low-altitude platform for relieving the strain on traditional ground cellular systems. However, the on-demand deployment of the UAV-assisted wireless networks (OWN) becomes an essential yet challenging issue, as the constraints of UAVs’ location, resource provisioning, and demand distribution should be jointly considered. In this work, we investigate the OWN problem by proposing an autonomous learning framework (ALF) consisting of three sequential stages: demand prediction, proactive deployment, and resource allocation fine-tuning, which can be capable of autonomous network planning without reliance on manual operations in an extremely dynamic environment. In the demand prediction stage, we first design a dual transformer network (DTN) to capture the temporal and spatial dependencies of wireless traffic. We further reduce the computational complexity of DTN from quadratic time complexity to log-linear time complexity. In the proactive deployment stage, we jointly optimize the UAVs’ location and resource provisioning by proposing a modified general benders decomposition algorithm with a Γ-optimal convergence, where a learning-based discerning module is designed to accelerate the algorithm. In the resource allocation fine-tuning stage, we propose a simulated annealing-based algorithm to minimize the transmission rate degradation of users to reduce the bias caused by traffic demand prediction. Extensive numerical results based on an open source dataset demonstrate the effectiveness of the proposed methods in comparison with existing baselines.

Sample-Based Neural Approximation Approach for Probabilistic Constrained Programs.

This article introduces a neural approximation-based method for solving continuous optimization problems with probabilistic constraints. After reformulating the probabilistic constraints as the quantile function, a sample-based neural network model is used to approximate the quantile function. The statistical guarantees of the neural approximation are discussed by showing the convergence and feasibility analysis. Then, by introducing the neural approximation, a simulated annealing-based algorithm is revised to solve the probabilistic constrained programs. An interval predictor model (IPM) of wind power is investigated to validate the proposed method.

Southbound Message Delivery With Virtual Network Topology Awareness in Clouds

Southbound message delivery from the control plane to the data plane is one of the essential issues in multi-tenant clouds. A natural method of southbound message delivery is that the control plane directly communicates with compute nodes in the data plane. However, due to the large number of compute nodes, this method may result in massive control overhead. The Message Queue (MQ) model can solve this challenge by aggregating and distributing messages to queues. Existing MQ-based solutions often perform message aggregation based on the physical network topology, which do not align with the fundamental requirements of southbound message delivery, leading to high message redundancy on compute nodes. To address this issue, we design and implement VITA, the first-of-its-kind work on virtual network topology-aware southbound message delivery. However, it is intractable to optimally deliver southbound messages according to the virtual attributes of messages. Thus, we design two algorithms, submodular-based approximation algorithm and simulated annealing-based algorithm, to solve different scenarios of the problem. Both experiment and simulation results show that VITA can reduce the total traffic amount of redundant messages by 45%-75% and reduce the control overhead by 33%-80% compared with state-of-the-art solutions.

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR - Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

A robust solution for optimizing facility location and network design with diverse link capacities

In this paper, the authors proffer a novel mathematical model for the simultaneous optimization of facility location and network design in the presence of uncertainty, with the aim of minimizing operational and transportation costs. The proposed model constitutes a departure from conventional methods in its consideration of probable events in the real world and the incorporation of uncertainty assumptions into the mathematical framework. An algorithm based on simulated annealing is then advanced for the solution of the problem, and the performance of the algorithm is evaluated through comparison with exact methods for problems of modest size, as well as with a basic simulated annealing algorithm for larger problems. The results of these comparisons demonstrate the superiority of the proposed meta-heuristic algorithm. Finally, the robust approach is compared with four other approaches in the presence of uncertainty, with a thorough analysis of the results obtained from each of the methods conducted in a suite of sample problems.

The multi-depot general share-a-ride problem

This research introduces the multi-depot general share-a-ride problem (MD-GSARP), which is an extension of the general share-a-ride problem (G-SARP). The goal of MD-GSARP is to maximize the profit from serving parcel and passenger requests with taxis that depart from several depots and return to their respective depots. We developed a mixed integer linear program for MD-GSARP and proposed a Simulated Annealing (SA) algorithm to solve the problem. The proposed algorithm consists of a deterministic insertion heuristic, a time slack strategy, two sets of neighborhood moves that depend on the time slack strategy, and a penalty mechanism for infeasible solutions. Computational results show that both the commercial solver CPLEX and our proposed SA achieve optimal solutions to small benchmark instances. Moreover, comparative results show that the proposed SA algorithm yields better solutions than the basic SA algorithm. Our analysis also reveals that MD-GSARP solutions are better than G-SARP solutions.

Clique Finder

The Maximum Clique Problem (MCP) is a classical NP-hard problem that has gained considerable attention due to its numerous real-world applications and theoretical complexity. It is inherently computationally complex, and so exact methods may require prohibitive computing time. Nature-inspired meta-heuristics have proven their utility in solving many NP-hard problems. In this research, we propose a simulated annealing-based algorithm that we call Clique Finder algorithm to solve the MCP. Our algorithm uses a logarithmic cooling schedule and two moves that are selected in an adaptive manner. The objective (error) function is the total number of missing links in the clique, which is to be minimized. The proposed algorithm was evaluated using benchmark graphs from the open-source library DIMACS, and results show that the proposed algorithm had a high success rate.

Timing-Oriented Task Offloading Algorithms for Internet-of-Vehicles

The emergence of computation intensive automotive applications poses significant challenges on computation capacity of automotive electronic systems, thus vehicular edge computing (VEC) is introduced as a new computing paradigm into the internet of vehicle (IoV) to improve its data processing capability. However, as the computation capacity is limited in VEC servers, efficient task offloading algorithms need to be proposed. This paper first proposes a multi-task offloading model and gives the related task response time analysis method, and then, both a mixed-integer linear programming (MILP)-based algorithm and a simulated annealing-based heuristic algorithm are proposed to minimize the task response time. By comparing with a baseline algorithm, the MILP-based offloading algorithm can reduce the average task response time by 91.45%, and the heuristic offloading algorithm can reduce the average task response time by 70%.

A new dynamic shape adjustment and placement algorithm for 3D yard allocation problem with time dimension

This paper studies a yard allocation problem at a container terminal, namely the 3D yard allocation problem with time dimension (3DYAPT), that determines the container storage locations in a given storage block to satisfy requirements from batches of arrived containers. The objective is to minimize the two-dimensional area of the storage block occupied for temporarily storing the containers within a given planning horizon (time dimension). The 3DYAPT is challenging and proved to be strongly NP-hard since it requires dynamically adjusting the shape of the allocated area when placing containers from the same request. We formulate the 3DYAPT as an integer linear programming model and develop a simulated annealing-based dynamic shape adjustment and placement algorithm (SA-DSAP). The simulated annealing-based algorithm comprises a novel dynamic programming procedure with several speed-up techniques that sequentially computes the storage space solution given a particular sequence of requests. Extensive computational experiments are conducted, showing that SA-DSAP is capable of finding optimal solutions very efficiently for nearly all small instances. For large instances, we also find that SA-DSAP produces significantly better heuristic solutions than the existing algorithm from the literature.

Clique Finder

The Maximum Clique Problem (MCP) is a classical NP-hard problem that has gained considerable attention due to its numerous real-world applications and theoretical complexity. It is inherently computationally complex, and so exact methods may require prohibitive computing time. Nature-inspired meta-heuristics have proven their utility in solving many NP-hard problems. In this research, we propose a simulated annealing-based algorithm that we call Clique Finder algorithm to solve the MCP. Our algorithm uses a logarithmic cooling schedule and two moves that are selected in an adaptive manner. The objective (error) function is the total number of missing links in the clique, which is to be minimized. The proposed algorithm was evaluated using benchmark graphs from the open-source library DIMACS, and results show that the proposed algorithm had a high success rate.

A simulated annealing-based algorithm for selecting balanced samples

Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

MobiPlace: Mobility-Aware Controller Placement in Software-Defined Vehicular Networks

In this paper, we propose a mobility-aware scheme, named MobiPlace, to address the controller placement problem (CPP) at the Road Side Units (RSUs) in Software-Defined Vehicular Networks (SDVNs). MobiPlace places local controllers at the selected RSUs to reduce the operational delay experienced in traditional SDVN architecture, where controllers are placed at the cloud. Additionally, we consider the effect of dynamic road traffic and propose dynamic adjustment of the placement of the controller with minimal changes. In contrast to the existing literature, we infuse traffic monitoring and traffic prediction strategies to ensure accurate delivery of control messages and data packets. We formulate an Integer Linear Program (ILP) and propose a solution approach consisting of three modules — mobility management , controller placement , and controller selection . The mobility management module uses Markov predictor to predict vehicle movement and reduce controller synchronization overhead when a vehicle moves to a different controller's coverage area. The controller placement module applies a simulated annealing-based algorithm to select potential RSUs that serve as local controllers. The controller selection module determines the preferable controller location for processing the service requests. Simulation results depict that MobiPlace reduces the average flow setup delay by $13.85 \%$ compared to the existing state-of-the-art.

Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.

A simulated annealing for a daily operating room scheduling problem under constraints of uncertainty and setup

This article addresses the daily scheduling of surgeries in an operating theatre. The problem consists of a set of surgical operations that have to be scheduled in identical operating rooms. The operations have uncertain durations, require setup activities before and after the surgical act and are assigned to surgeons who have to perform the surgical part. The objective is to find the best sequence of operations in each room in order to minimize the theatre’s opening duration (makespan) and the surgeons’ waiting times. A stochastic mathematical model and a simulated annealing-based algorithm, where the uncertainty is represented using the sample average approximation technique, are provided. An experimental study carried out on real-sized instances shows that the method’s solutions have respectable relative gaps in comparison with lower bounds and optimal solutions.

A simulated annealing approach for resolution guided homogeneous cryo-electron microscopy image selection.

Cryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET) have emerged as important imaging techniques for studying structures of macromolecular complexes. In 3D reconstruction of large macromolecular complexes, many 2D projection images of macromolecular complex particles are usually acquired with low signal-to-noise ratio. Therefore, it is meaningful to select multiple images containing the same structure with identical orientation. The selected images are averaged to produce a higher-quality representation of the underlying structure with improved resolution. Existing approaches of selecting such images have limited accuracy and speed. We propose a simulated annealing-based algorithm (SA) to pick the homogeneous image set with best average. Its performance is compared with two baseline methods based on both 2D and 3D datasets. When tested on simulated and experimental 3D Cryo-ET images of Ribosome complex, SA sometimes stopped at a local optimal solution. Restarting is applied to settle this difficulty and significantly improved the performance of SA on 3D datasets. Experimented on simulated and experimental 2D Cryo-EM images of Ribosome complex datasets respectively with SNR = 10 and SNR = 0.5, our method achieved better accuracy in terms of F-measure, resolution score, and time cost than two baseline methods. Additionally, SA shows its superiority when the proportion of homogeneous images decreases. SA is introduced for homogeneous image selection to realize higher accuracy with faster processing speed. Experiments on both simulated and real 2D Cryo-EM and 3D Cryo-ET images demonstrated that SA achieved expressively better performance. This approach serves as an important step for improving the resolution of structural recovery of macromolecular complexes captured by Cryo-EM and Cryo-ET.

Machine Cell Formation for Dynamic Part Population Considering Part Operation Tradeoff and Worker Assignment Using Simulated Annealing based Genetic Algorithm

In this study, an integrated mathematical model for the cell formation problem is proposed considering the dynamic production environment. The proposed model yields, manufacturing cells, part families and worker's assignment simultaneously by allowing a cubic search space of 'machine-part-worker' in the CMS. The resources are aggregated into manufacturing cells based on the optimal process route among the user specified multiple routes. The model interprets flexibility in the processing of subsets of a part operation sequence in the different production mode (internal production/subcontracting part operation). It is a tangible advantage during unavailability of worker and unexpected machine break down occurring in the real world. The proposed cell formation problem has been solved by using a simulated annealing-based genetic algorithm (SAGA). The algorithm imparts synergy effect to improve intensification, diversification in the cubic search space and increases the possibility of achieving near-optimum solutions. To evaluate the computational performance of the proposed approach the algorithm is tested on a number of randomly generated instances. The results substantiate the efficiency of the proposed approach by minimising overall cost. [Received: 17 August 2018; Accepted: 28 July 2019]

Optimal Blocking Device Placement for Geomagnetic Disturbance Mitigation

Geomagnetically induced currents (GICs) introduced by geomagnetic disturbances (GMDs) can damage transformers, increase reactive power losses, and cause reliability issues in power systems. Finding an optimal strategy to place blocking devices (BDs) at transformer neutrals is essential to mitigating the negative impact of GICs. In this paper, we study and solve the problem of optimal BD placement (OBP), aiming at minimizing the damages of GMDs, subject to various practical constraints. In particular, our approach accounts for the potential impact of BD placement to neighboring interconnected systems; we also consider the time-varying nature of the geoelectric field in BD placement. To the best of our knowledge, neither has been considered in the existing works. Under these constraints, the combined complexity of solving the OBP problem on a large-scale system poses a big challenge. To address this, we developed a simulated annealing-based algorithm that can achieve near-optimal solutions for the OBP problem at a reduced computational complexity, while taking the above constraints into account. More importantly, this paper provides a general mathematical framework that can be used to solve various OBP problems, with different objective functions and constraints. We demonstrated the effectiveness and the efficiency of our method by using power systems of various sizes.

Content Propagation for Content-Centric Networking Systems From Location-Based Social Networks

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users’ interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%–65.6%, and the contents can be disseminated faster by about 24.6%–57.8%.

Postmortem evidence of neuroinflammation in bipolar disorder: a systematic review

Permanent breast seed implant (PBSI) is a developing brachytherapy technique for the treatment of early-stage breast cancer. In current practice, PBSI uses manual planning strategies to generate clinical treatment plans. In this work, a simulated annealing-based algorithm is developed to demonstrate the first application of inverse optimization for PBSI.Target, skin, and chest wall muscle contours, exported from a treatment planning system in digital imaging and communications in medicine format, are used as inputs. To optimize, the user defines the dose–volume histogram objectives for the target and specifies a relative weighting for target and skin constraints. A 10-patient cohort of previously treated patients was planned by using the inverse optimization algorithm. Plan quality was compared to the clinically treated manually generated plans using the V90%, V100%, V150%, and V200% for the planning target volume (PTV), V90% and D0.2 cc for skin dose, and PTV conformity indices.For each of the 10 patients, patient-wise paired differences between inverse and manual plans were analyzed and presented in box plots. Comparing inverse and manual planning techniques, a statistical difference was not observed (p > 0.05) in PTV coverage criteria (V90%, V100%) and dose to skin2mm. A statistical difference was observed in the inverse plans as a reduction of the V150% (mean of 6.2%) and increase in conformity index of the 20%, 50%, 90%, and 100% isodose lines.This work presents the first application of inverse optimization used to generate PBSI treatment plans. A 10-patient cohort previously treated with PBSI was retrospectively planned for comparison with the clinically treated manually generated plans.

Request Delay-Based Pricing for Proactive Caching: A Stackelberg Game Approach

Proactively pushing content to users has emerged as a promising approach to improve the spectrum usage in off-peak times for fifth-generation mobile networks. However, owing to the uncertainty of future user demands, base stations (BSs) may not receive payments for the pushed files. To motivate content pushing, providing economic incentives to BSs becomes essential. Based on request delay information (RDI) that characterizes the users’ request time for content files, this paper studies the profit maximization for a BS and a spectrum provider (SP) by developing a Stackelberg game. Specifically, the SP sets different selling prices of bandwidth for pushing and on-demand services, while the BS responds with the optimal quantity to purchase. In the game with non-causal RDI, a sub-gradient algorithm is presented to achieve a Stackelberg equilibrium (SE). For the game with statistical RDI, a closed-form expression is derived for an SE in the single-user scenario and a simulated annealing-based algorithm is designed to obtain an SE in the multi-user scenario. It is shown that the proposed games achieve greater profit for both the SP and the BS, compared with the on-demand scheme. Furthermore, pricing with statistical RDI attains performance closely approaching that with non-causal RDI, while being more practical.