Problem-specific Genetic Algorithm Research Articles

Model-based estimation of AV-nodal refractory period and conduction delay trends from ECG.

Introduction: Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to patients and the healthcare system. The atrioventricular (AV) node plays a vital role in regulating heart rate during AF by filtering electrical impulses from the atria. However, it is often insufficient in regards to maintaining a healthy heart rate, thus the AV node properties are modified using rate-control drugs. Moreover, treatment selection during permanent AF is currently done empirically. Quantifying individual differences in diurnal and short-term variability of AV-nodal function could aid in personalized treatment selection. Methods: This study presents a novel methodology for estimating the refractory period (RP) and conduction delay (CD) trends, and their uncertainty in the two pathways of the AV node during 24 h using non-invasive data. This was achieved by utilizing a network model together with a problem-specific genetic algorithm and an approximate Bayesian computation algorithm. Diurnal variability in the estimated RP and CD was quantified by the difference between the daytime and nighttime estimates, and short-term variability was quantified by the Kolmogorov-Smirnov distance between adjacent 10-min segments in the 24-h trends. Additionally, the predictive value of the derived parameter trends regarding drug outcome was investigated using several machine learning tools. Results: Holter electrocardiograms from 51 patients with permanent AF during baseline were analyzed, and the predictive power of variations in RP and CD on the resulting heart rate reduction after treatment with four rate control drugs was investigated. Diurnal variability yielded no correlation to treatment outcome, and no prediction of drug outcome was possible using the machine learning tools. However, a correlation between the short-term variability for the RP and CD in the fast pathway and resulting heart rate reduction during treatment with metoprolol (ρ = 0.48, p < 0.005 in RP, ρ = 0.35, p < 0.05 in CD) were found. Discussion: The proposed methodology enables non-invasive estimation of the AV node properties during 24 h, which-indicated by the correlation between the short-term variability and heart rate reduction-may have the potential to assist in treatment selection.

Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm

AbstractAssembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to $$\sim $$ ∼ 2.75$$\times $$ × , reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

ECG based assessment of circadian variation in AV-nodal conduction during AF-Influence of rate control drugs.

The heart rate during atrial fibrillation (AF) is highly dependent on the conduction properties of the atrioventricular (AV) node. These properties can be affected using β-blockers or calcium channel blockers, mainly chosen empirically. Characterization of individual AV-nodal conduction could assist in personalized treatment selection during AF. Individual AV nodal refractory periods and conduction delays were characterized based on 24-hour ambulatory ECGs from 60 patients with permanent AF. This was done by estimating model parameters from a previously created mathematical network model of the AV node using a problem-specific genetic algorithm. Based on the estimated model parameters, the circadian variation and its drug-dependent difference between treatment with two β-blockers and two calcium channel blockers were quantified on a population level by means of cosinor analysis using a linear mixed-effect approach. The mixed-effects analysis indicated increased refractoriness relative to baseline for all drugs. An additional decrease in circadian variation for parameters representing conduction delay was observed for the β-blockers. This indicates that the two drug types have quantifiable differences in their effects on AV-nodal conduction properties. These differences could be important in treatment outcome, and thus quantifying them could assist in treatment selection.

A multi-stage heuristic algorithm based on task grouping for vehicle routing problem with energy constraint in disasters

In recent years, serious disasters have happened frequently, causing significant loss of lives and substantial economic consequences. Considering that rescue vehicles are mainly powered by electricity, they cannot compete with the conventional ones in terms of the cruising range. Therefore, it is of great significance to study how rescue vehicles can execute multiple rescue tasks quickly and efficiently with energy constraint. In this paper, a multi-stage vehicle routing algorithm based on task grouping (MSVR-TG) is proposed for the rescue vehicle routing problem with energy constraint (VRPEC) in disasters. In the task grouping stage, a novel K-means algorithm based on angular density (K-means-ad) is suggested to address the instability of the result of K-means caused by the random selection of initial cluster centers. It can balance the travel distance of each vehicle as much as possible, which is critical when vehicles are energy constrained. In the sequence planning stage, a problem-specific genetic algorithm (PSGA) with multiple population initialization rules and the route improvement strategy is introduced to balance the convergence speed and population diversity, achieving a promising planning result. In the route adjusting stage, various removal and insertion heuristics in the large neighborhood search (LNS) are designed to adjust the routes, thereby further improving the planning result. The routes participating in adjustment are obtained via the developed route selection rule. The validity of each stage in MSVR-TG has been verified in a series of scenarios. A comparison about the proposed algorithm against several advanced algorithms is constructed, and results reveal that MSVR-TG is superior over compared algorithms for VRPEC.

An optimization approach for multi-echelon supply chain viability with disruption risk minimization

The outbreak of extraordinary disruptive events, e.g., the COVID-19 pandemic, has greatly impacted the orderly operation in global supply chains (SCs), and may lead to the SC breakdown. Regulatory actions, such as government interventions during the pandemic, can greatly mitigate the disruption propagation (i.e., the ripple effect) and improve SC viability. However, existing works that focus on the disruption propagation management have not considered the possibility of such interventions. Motivated by the fact, in this study, we investigate a new disruption propagation management problem in a multi-echelon SC with limited intervention budget. The aim is to minimize disruption risk measured by the disrupted probability of target participants in the SC. For the problem, a novel approach, combining the Causal Bayesian Network (CBN), the do-calculus and the mathematical programming, is developed. Specially, two mixed-integer non-linear programming models are constructed to determine appropriate interventions. To enhance the proposed mathematical models, two valid inequalities are proposed. Then, a problem-specific genetic algorithm (GA) is developed for handling large-scale problem instances. Numerical experiments on a case study and randomly generated instances are conducted to evaluate the efficiency of the proposed models, the valid inequalities and the GA. Based on experiment analysis, managerial insights are drawn.

Matching Cyber Security Ontologies through Genetic Algorithm-Based Ontology Alignment Technique

Security ontology can be used to build a shared knowledge model for an application domain to overcome the data heterogeneity issue, but it suffers from its own heterogeneity issue. Finding identical entities in two ontologies, i.e., ontology alignment, is a solution. It is important to select an effective similarity measure (SM) to distinguish heterogeneous entities. However, due to the complex semantic relationships among concepts, no SM is ensured to be effective in all alignment tasks. The aggregation of SMs so that their advantages and disadvantages complement each other directly affects the quality of alignments. In this work, we formally define this problem, discuss its challenges, and present a problem-specific genetic algorithm (GA) to effectively address it. We experimentally test our approach on bibliographic tracks provided by OAEI and five pairs of security ontologies. The results show that GA can effectively address different heterogeneous ontology-alignment tasks and determine high-quality security ontology alignments.

Non-invasive Characterization of Human AV-Nodal Conduction Delay and Refractory Period During Atrial Fibrillation.

During atrial fibrillation (AF), the heart relies heavily on the atrio-ventricular (AV) node to regulate the heart rate. Thus, characterization of AV-nodal properties may provide valuable information for patient monitoring and prediction of rate control drug effects. In this work we present a network model consisting of the AV node, the bundle of His, and the Purkinje fibers, together with an associated workflow, for robust estimation of the model parameters from ECG. The model consists of two pathways, referred to as the slow and the fast pathway, interconnected at one end. Both pathways are composed of interacting nodes, with separate refractory periods and conduction delays determined by the stimulation history of each node. Together with this model, a fitness function based on the Poincaré plot accounting for dynamics in RR interval series and a problem specific genetic algorithm, are also presented. The robustness of the parameter estimates is evaluated using simulated data, based on clinical measurements from five AF patients. Results show that the proposed model and workflow could estimate the slow pathway parameters for the refractory period, and ΔRSP, with an error (mean ± std) of 10.3 ± 22 and −12.6 ± 26 ms, respectively, and the parameters for the conduction delay, and , with an error of 7 ± 35 and 4 ± 36 ms. Corresponding results for the fast pathway were 31.7 ± 65, −0.3 ± 77, 17 ± 29, and 43 ± 109 ms. These results suggest that both conduction delay and refractory period can be robustly estimated from non-invasive data with the proposed methodology. Furthermore, as an application example, the methodology was used to analyze ECG data from one patient at baseline and during treatment with Diltiazem, illustrating its potential to assess the effect of rate control drugs.

Parallel Machine Scheduling with Due Date-to-Deadline Window, Order Sharing and Time Value of Money

Motivated by a variety of applications in sharing economy, we study an identical parallel machine scheduling problem with due date-to-deadline window by jointly considering machine sharing and the time value of money. A factory owns a set of parallel identical machines and processes a set of production orders within a finite time period. In the sharing setting, the factory may also rent external machines to handle a part of orders by paying some extra cost. The factory aims to determine the sharing policy of the production orders and the scheduling rule of machines, to maximize its total future value of profits by satisfying the orders. To the best of our knowledge, there are no previous results for this problem. In this work, a mathematical programming model is derived, and a problem-specific genetic algorithm and a heuristic are proposed to solve large-scale instances. Numerical experiments using randomly generated instances are carried out to evaluate the effectiveness and efficiency of the proposed solution methods.

The optimization of UAV routing problem with a genetic algorithm to observe the damages of possible Istanbul earthquake

In this study, the problem is to find a route for a UAV that takes off from Istanbul to observe the damages that may occur after the possible Istanbul earthquake within the first 24 hours. In the problem, 230 candidate grid points that UAV can visit on Istanbul are determined and the weight values combining the risk values based on earthquake degree zones and the population densities of the grid points are calculated for each candidate point. It is aimed to find a route for the UAV to maximize the total weights of the visited grid points under the UAV range constraint. The described problem is adapted to the Orienteering Problem in the literature. Since the Orienteering Problem is an NP-hard problem, a problem-specific genetic algorithm and a simulated annealing algorithm are developed to solve the problem. The parameters of the algorithms are tuned by experiments. 15 different scenarios including the daily number of visits (of taken images) and the airports that the UAV takes and lands off after the earthquake are created and tried to be solved exactly via ILOG and approximately via developed metaheuristics. While the optimal solutions are found for 2 of 15 scenarios via ILOG, the designed genetic algorithm has better solutions and can solve the problem within acceptable CPU times for the rest of the scenarios.

Simultaneous structuring and scheduling of multiple projects with flexible project structures

We study the problem to simultaneously decide on the structures and the schedules for an entire portfolio of flexible projects. The projects are flexible as alternative technologies and procedures can be used to achieve the respective project task. The choice between different technologies and procedures affects the activities to be implemented and thus the precedence relations, i.e., the structure of the project. The different projects have given due dates with specific delay payments and compete for scarce resources. In this situation, project structure decisions and scheduling decisions are highly intertwined and have to be made simultaneously in order to achieve the assumed objective of minimizing the delay payments for the entire project portfolio. The problem is formally stated and solved via novel and problem-specific genetic algorithms. The performance of the new algorithms is evaluated with respect to speed and accuracy in a systematic and comprehensive numerical study.

IoT-based location and quality decision-making in emerging shared parking facilities with competition

Shared parking firms offer a double-sided platform for parking space sharing. Many of these firms provide differentiated service levels to both suppliers and buyers. This new phenomenon in the parking industry materialized thanks to recent innovations in IoT-enabled automation and electric vehicle charging technologies. We study shared parking firms. Specifically, we formulate the firm's location and quality decision problem by using a multiplicative interaction model with competition. A non-cooperative game renders the optimized quality levels and location selections at Nash equilibrium in the presence of competition. We illustrate managerial insights with a small-sized problem. For industry practitioners, we propose a tailored branch and bound based exact algorithm and a problem-specific genetic algorithm for large-sized problems. Simulated computational results confirm the effectiveness and efficiency of the proposed shared-parking decision support model.

A dynamic model for real-time track assignment at railway yards

In this paper, we study the real-time train assignment problem (RT-TAP) that arises from the unreliable arrival times of freight trains and the last-minute parking requests at railway yards. In the RT-TAP, the reassignment of trains to the yard is triggered every time a train arrives at the railway yard and needs to be assigned (event-based optimization). After introducing a problem formulation for the RT-TAP problem, we prove that RT-TAP is NP-Hard. In particular, the RT-TAP is modeled as a mixed integer program that strives to minimize the total weighted delay of trains. Because of its computational complexity and the time-critical nature of this problem, we introduce two real-time solution methods: (a) a problem-specific genetic algorithm (GA), (b) and a first-scheduled first-served (FSFS) heuristic. In small instances, we show that the GA returns a globally optimal solution which is identical to the solution of exact optimization methods. In larger problem instances, the heuristic approaches of FSFS and GA are tested at the Waalhaven Zuid railway yard in the Netherlands using two months of operational data. In the experimental results, the GA solutions reduce the average delays by more than 4 min compared to the solutions of the FSFS heuristic.

Reliable wireless multimedia sensor network design: comparison of hybrid metaheuristics and a matheuristic

Network reliability has vital importance for designing wireless multimedia sensor networks (WMSNs). The definition of network reliability for WMSNs differentiates from the traditional communication network types which simultaneously involves node deployment, connectivity and coverage issues. Therefore, in this study, a new information gathering network reliability definition is made for WMSNs. The information gathering network reliability is maximized under a given total budget constraint by including node and terrain characteristics. The model is developed to get surveillance from an enemy zone. Since the reliable WMSN design considering node deployment, connectivity and coverage has NP-hard complexity, new hybrid methods are proposed with hybridization of exact methods with nature-inspired metaheuristics. Five algorithms are generated. Firstly, problem-specific simulated annealing (SA) and genetic algorithm (GA) are developed, then branch and bound (B&B) is embedded into the SA and GA named as hybrid SA (HSA) and hybrid GA (HGA). The B&B method optimizes the orientations of the sensor nodes. Additionally, an HGA-based matheuristic (HGABM) is proposed. In HGABM, a mixed integer linear programming (MILP) network flow-based model is added into the initial population generation procedure of the HGA. The MILP model finds the exact deployment points of the relay nodes. In experimental study, it is noticed that the main time-consuming parts of the algorithms are network reliability calculations. Thence, a parallel Monte Carlo (MC) simulation is developed and the MC runs are made in multiple general purpose graphics processing units (GPGPUs). Full-factorial experimental design and Taguchi design approaches are preferred to tune the parameters, to generate the problem sets and to make the experiments. The experimental study is performed on synthetically generated terrains with different terrain and device-based scenarios. Statistical methods are used to compare the performances of the algorithms. In conclusion, for small-sized sets HGABM and for moderate- and large-sized sets, HGA outperforms the other algorithms. The algorithms are coded in MATLAB and the MILP model is solved with CPLEX.

Coke production scheduling problem: A parallel machine scheduling with batch preprocessings and location-dependent processing times

In this paper, an integer programming model is developed for a newly addressed coke production scheduling problem, in which two typical characteristics are considered: (i) The transportation of raw coal by a vehicle causes a batch preprocessing; (ii) The heating of raw coal by closely located coke ovens may extend the processing times of cokes, under the temperature influence. To the best of our knowledge, such a geographically location-dependent processing time has not been studied. The purpose is to minimize the completion time of the last coke among all ovens, i.e., the makespan. Therefore, the problem of interest can be viewed as a parallel machine makespan minimization scheduling problem, featured with batch preprocessings and location-dependent processing times. For this NP-hard problem, a problem-specific genetic algorithm and a fast heuristic are devised to enhance the computational efficiency. Experimental results on 330 randomly generated instances show the effectiveness and efficiency of the proposed solution methods.

Spatial Optimization for the Planning of Sparse Power Distribution Networks

This paper presents a novel method to determine the optimal routing of medium voltage distribution networks in sparse rural areas. The objective is evaluated through minimizing the net present cost of the network over a selected time period. A problem specific genetic algorithm is proposed to address the optimal network routing problem and compute the optimized topology throughout a geographically constrained region. The proposed method simultaneously considers nonfixed candidate lines to overcome search space restrictions through a variable length encoding structure and the use of Steiner points, and a shortest path algorithm to traverse between point-to-point connections in the constrained region. Geographical restrictions on network routing are considered through the formation of a rasterized map. The network is modeled at the branch level and considers both greenfield and expansion planning to highlight the effects of accessibility restrictions. The optimization model is applied to a real rural distribution network in the South–West of Western Australia. Alternative network topologies are found to provide significant improvements over the existing network and traditional reconfiguration based methods for evaluating the minimum cost sparse rural distribution network.

Lot sizing and supplier selection with multiple items, multiple periods, quantity discounts, and backordering

Abstract A general model is presented for a realistic multi-item lot-sizing problem with multiple suppliers, multiple time periods, quantity discounts, and backordering of shortages. Mixed integer programming (MIP) is used to formulate the problem and obtain the optimum solution for smaller problems. Due to the large number of variables and constraints, the model is too hard to solve optimally for practical problems. In order to tackle larger problem sizes, two heuristic solution methods are proposed. The first method is developed by modifying the Silver-Meal heuristic, and the second one by developing a problem-specific Genetic Algorithm (GA). Both heuristic methods are shown to be effective in solving the lot-sizing problem, but the GA is generally superior.

An investment decision model for the optimal placement of phasor measurement units

Safe operation and reliability of the electrical power systems necessitate full observability of the power grid. Phasor measurement units (PMUs) are the state-of-the-art intelligent devices that collect synchronized phasors of voltages and currents in real time. It is not economically justifiable to install PMUs at all buses of the power grid. Hence, designing the PMUs network and determining their optimal placement in the power grid is an investment decision. In this paper, we propose a new investment decision model to determine the optimal placement of PMUs that guarantees the full observability of the power grid. Network observability rules are applied to reduce the capital cost of installing PMUs. A problem-specific genetic algorithm is developed to determine the optimal investment decision. The N-1 reliability requirement of the power grid has been integrated in the model as well to obtain the resilient network design against all single contingencies such as failure of a PMU or a transmission line. Furthermore, a two-phase investment plan is proposed, which provides the power system investors with more flexibility and avoids unnecessary investment costs. In the first phase, PMUs are installed to achieve full observability of the power grid whereas additional PMUs will be installed in the second phase to guarantee the full observability in case of single contingencies. To test the efficacy of the proposed model, experiments are conducted on multiple IEEE test systems with and without considering zero-injection buses. The results are compared to the other methods such as integer linear programming and heuristic methods. The analysis shows that the proposed approach is promising and verifies its efficacy.

Genetic algorithm for chance constrained reliability stochastic optimisation problems

This paper addresses the chance constrained reliability stochastic optimisation problem, in which the objective is to maximise system reliability for the given chance constraints. A problem specific stochastic simulationbased genetic algorithm (GA) is developed for finding optimal redundancy to an n-stage series system with m -chance constraints of the redundancy allocation problem. As GA is a proven robust evolutionary optimisation search technique for solving various reliability optimisation problems and the Monte Carlo (MC) simulation, which is a flexible tool for checking feasibility of chance constraints, we have effectively combined GA and MC simulation in the proposed algorithm. The effectiveness of the proposed algorithm is illustrated for a four-stage series system with two chance constraints.

Optimization of layered multicast with network coding and genetic algorithm

This article studies the problem of constructing optimal layered multicast with network coding for heterogeneous networks. Based on the flexibility of layered source coding, a global-favorable optimization scheme is proposed, which maximizes the aggregate throughput of heterogeneous sink nodes for layered multicast with network coding by determining the optimal bit rates of the layers. To solve this global-favorable optimization scheme, especially in the large-scale heterogeneous networks, a new problem-specific genetic algorithm (GA) is further proposed. It not only searches efficiently for the optimal allocation of layer bit rates, but also guarantees the validity of candidate solutions in the whole evolutionary process. Simulation results demonstrate that this new GA-based optimization scheme could obtain efficiently the optimal or satisfactorily near-optimal bit rates for layered multicast with network coding, even in the large-scale heterogeneous networks.

A Rough Programming Approach to Power-Balanced Instruction Scheduling for VLIW Digital Signal Processors

The focus of this paper is on VLIW instruction scheduling that minimizes the variation of power consumed by the processor during the execution of a target program. We use rough set theory to characterize the imprecision inherent in the instruction-level power model that is obtained through empirical measurements. The optimal instruction scheduling problem based on such a power model is formulated as a chance-constrained rough program which is solved by a problem-specific genetic algorithm. Efficiency of the algorithm is greatly improved through a novel rule-based approach to rank the intermediate candidate schedules. Experimental results using the MediaBench and Trimaran benchmarks show that the near-optimal schedules obtained are significantly better than those obtained through the mixed-integer programming approach. Computational requirements are low enough for the technique to be adopted by practical compilers.