Combinatorial Optimization Scheme Research Articles

Making Cities Smarter-Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints.

One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

Hierarchical combinatorial design and optimization of non-periodic metamaterial structures

Abstract As advanced manufacturing techniques like additive manufacturing have enabled the fabrication of complex geometries, it is worthwhile to explore potential techniques that can be used to design metamaterial structures to achieve desired behaviors or physical properties. Designing the fine-scale geometry of a metamaterial structure to perform a specific behavior defines an inverse problem that is often intractable. To make this inverse problem tractable, most researchers represent metamaterial structures relying on a single-type periodic unit cell that is repeated in regular grids. Such a representation simplifies modeling and simulation tasks but at the cost of conceivably limiting the spectrum of physical properties that can be achieved by using more than one type of unit cells. This paper defines a non-periodic representation via implicit functions that generate two types of unit cells in pairs, which cope with the geometric frustration of interface connection in non-periodic metamaterial design. To solve the inverse problem of non-periodic metamaterial design, we propose a genetic algorithm-based combinatorial optimization scheme to design light-weight and high-strength functional parts by optimizing relevant parameters in the generating functions of non-periodic unit cells.

Sensitivity studies and optimization of arrangements of optically pumped magnetometers in simulated magnetoencephalography

Purpose The purpose of this paper is to present a simulation study using a model of a new optically pumped magnetometer sensor for application in the field of magnetoencephalography. The effects of sensor distance and orientation on the measurement information and the sensitivity to neuronal sources are investigated. Further, this paper uses a combinatorial optimization approach for sensor placement to measure spontaneous activity in the region of the occipital cortex. Design/methodology/approach This paper studies the effects of sensor distance and orientation on sensitivity to cortical sources and measurement information. A three-compartment model of the head, using the boundary element method, is applied. For sensor setup optimization, a combinatorial optimization scheme is developed. Findings The sensor distance to sources considerably affects the sensitivity and the retrieved information. A specific arrangement of four sensors for measuring spontaneous activity over the occipital part of the head is optimized by effectively avoiding position conflicts. Research limitations/implications Individual head models, as well as more detailed noise and signal models, will increase the significance for specific-use cases in future studies. Originality/value Effects of sensor distance and orientation are specifically evaluated for a new optically pumped magnetometer. A discrete optimization scheme for sensor optimization is introduced. The presented methodology is applicable for other sensor characterization and optimization problems. The findings contribute significantly to the development of new sensors.

Development and application of a priority rated optimization model (PROM) for multi-sector water resource management systems

We present a new Priority Rated Optimization Model (PROM) for multi-sector water resource assessment, allocation and management. PROM utilizes flow duration curves (FDC), demand-to-supply ratio (DSR) and utility indices (UI) to evaluate and optimize priority based multi-sector water allocation. DSR links demand volume and priority with corresponding supply volume and likelihood derived from segmentation of FDC. The UI function assigns weights to ranges of DSR to assess the existing and potential alternative management scenarios. Supply reliability and UI values are used in a combinatorial optimization scheme to estimate allocated volume, flow rate and percent exceedance. PROM is applied in the Mara River Basin of Kenya/Tanzania, and results showed potential improvement over the current management practice towards sustainability of the Mara-Serengeti ecosystem. PROM bridges the gap between environmental and economic models while providing a user-friendly platform for direct stakeholder involvement to explore alternative water allocation scenarios.

Achieving Remission in Gulf War Illness: A Simulation-Based Approach to Treatment Design.

Gulf War Illness (GWI) is a chronic multi-symptom disorder affecting up to one-third of the 700,000 returning veterans of the 1991 Persian Gulf War and for which there is no known cure. GWI symptoms span several of the body’s principal regulatory systems and include debilitating fatigue, severe musculoskeletal pain, cognitive and neurological problems. Using computational models, our group reported previously that GWI might be perpetuated at least in part by natural homeostatic regulation of the neuroendocrine-immune network. In this work, we attempt to harness these regulatory dynamics to identify treatment courses that might produce lasting remission. Towards this we apply a combinatorial optimization scheme to the Monte Carlo simulation of a discrete ternary logic model that represents combined hypothalamic-pituitary-adrenal (HPA), gonadal (HPG), and immune system regulation in males. In this work we found that no single intervention target allowed a robust return to normal homeostatic control. All combined interventions leading to a predicted remission involved an initial inhibition of Th1 inflammatory cytokines (Th1Cyt) followed by a subsequent inhibition of glucocorticoid receptor function (GR). These first two intervention events alone ended in stable and lasting return to the normal regulatory control in 40% of the simulated cases. Applying a second cycle of this combined treatment improved this predicted remission rate to 2 out of 3 simulated subjects (63%). These results suggest that in a complex illness such as GWI, a multi-tiered intervention strategy that formally accounts for regulatory dynamics may be required to reset neuroendocrine-immune homeostasis and support extended remission.

Combinatorial Optimization of Exclusive Bus Lanes and Bus Frequencies in Multi-Modal Transportation Network

Bus priority strategy has widely been recognized as an effective countermeasure in the practical field of urban traffic congestion mitigation. This study proposes a bi-level programming model to comprehensively analyze the combinational optimization problem of exclusive bus lanes (EBLs) with variable bus frequencies (BFs) in multi-modal transportation networks. The objective function is to minimize the sum of the road users’ travel costs and the transit operating costs. The upper level model is an optimal decision-making program for setting up EBLs and BFs, and the lower level is a multi-modal transportation network equilibrium model. An efficient genetic algorithm is designed to solve the problem. The results of the numerical analysis show that the combinatorial optimization scheme of EBLs and BFs performs well with regard to the objective function in the model. The outperformance of combinatorial optimization scheme becomes greater with increased traffic demand, whereas oversetting of EBLs can result in reduced operating efficiency of the transportation system.

Three‐Dimensional Multiphase Segmentation of X‐Ray CT Data of Porous Materials Using a Bayesian Markov Random Field Framework

Advancements in noninvasive imaging methods such as X‐ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore‐scale fluid and interfacial dynamics to practical applications such as enhanced oil recovery and advanced contaminant remediation. While substantial efforts and resources have been devoted to advance CT technology, microscale analysis, and fluid dynamics simulations, the development of efficient and stable three‐dimensional multiphase image segmentation methods applicable to large data sets is lacking. To eliminate the need for wet–dry or dual‐energy scans, image alignment, and subtraction analysis, commonly applied in X‐ray micro‐CT, a segmentation method based on a Bayesian Markov random field (MRF) framework amenable to true three‐dimensional multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for three grayscale data sets consisting of dry glass beads, partially saturated glass beads, and partially saturated crushed tuff obtained with synchrotron X‐ray micro‐CT demonstrate great potential of the MRF image model for three‐dimensional multiphase segmentation. While our results are promising and the developed algorithm is stable and computationally more efficient than other commonly applied porous media segmentation models, further potential improvements exist for fully automated operation.

Application of multi-objective genetic algorithms to two case studies of reliability efficiency analysis and optimal expansion of electrical transmission networks

Two applications of multi-objective genetic algorithms to the analysis and optimization of electrical transmission networks are reported to show the potential of these combinatorial optimization schemes in the treatment of highly interconnected, complex systems. In a first case study, an analysis of the topological structure of an electrical power transmission system in the literature is carried out to identify the most important group of elements of different sizes in the network. The importance is quantified in terms of group closeness centrality. In the second case study, an optimization method is developed for identifying strategies of expansion of an electrical transmission network by addition of new lines of connection which are optimally identified with respect to the objective of improving the transmission reliability, while limiting the investment cost.

Single and multi-objective optimal design of water distribution systems: application to the case study of the Hanoi system

This manuscript describes the application of two recent methodologies developed by the authors for single and multi-objective optimal design of water distribution systems. The single-objective model is a hybrid algorithm incorporating decomposition, spanning tree search, and evolutionary computation, while the multi-objective algorithm integrates features form multi-objective genetic algorithms with the Cross Entropy combinatorial optimization scheme. The two models are implemented on the Hanoi water distribution system, one of the more explored systems in the research literature, through base runs and sensitivity analysis. The single-objective model produced the best known least cost solution for split pipe design, while the multi-objective model has shown robustness and well explanatory outcomes. Discussion of the accomplished results and suggestions for future research are provided.

Combinatorial individual differences scaling within the city-block metric

A new method is proposed for conducting individual differences scaling within the city-block metric that does not rely on gradient- or subgradient-based optimization. Instead, a combinatorial optimization scheme is utilized for identifying object coordinates minimizing the least-squares loss function. The illustrative application of combinatorial individual differences scaling within the city-block metric to schematic face stimuli suggests that the new method offers a promising alternative to gradient-based attempts for fitting city-block scaling models, which suffer from the well-documented difficulty of local minima.

A combinatorial optimization scheme for parameter structure identification in ground water modeling.

This research develops a methodology for parameter structure identification in ground water modeling. For a given set of observations, parameter structure identification seeks to identify the parameter dimension, its corresponding parameter pattern and values. Voronoi tessellation is used to parameterize the unknown distributed parameter into a number of zones. Accordingly, the parameter structure identification problem is equivalent to finding the number and locations as well as the values of the basis points associated with the Voronoi tessellation. A genetic algorithm (GA) is allied with a grid search method and a quasi-Newton algorithm to solve the inverse problem. GA is first used to search for the near-optimal parameter pattern and values. Next, a grid search method and a quasi-Newton algorithm iteratively improve the GA's estimates. Sensitivities of state variables to parameters are calculated by the sensitivity-equation method. MODFLOW and MT3DMS are employed to solve the coupled flow and transport model as well as the derived sensitivity equations. The optimal parameter dimension is determined using criteria based on parameter uncertainty and parameter structure discrimination. Numerical experiments are conducted to demonstrate the proposed methodology, in which the true transmissivity field is characterized by either a continuous distribution or a distribution that can be characterized by zones. We conclude that the optimized transmissivity zones capture the trend and distribution of the true transmissivity field.

Stochastic Reservoir Modeling Using Simulated Annealing and Genetic Algorithms

Summary This paper discusses and compares three different algorithms based on combinatorial optimization schemes for generating stochastic permeability fields. The algorithms are not restricted to generating Gaussian random fields and have the potential to accomplish geologic realism by combining data from many different sources. We have introduced a "heat-bath" algorithm for simulated annealing (SA) as an alternative to the commonly used "Metropolis" algorithm and a new stochastic modeling technique based on the "genetic" algorithm. We applied these algorithms to a set of outcrop and tracer flow data and examined the associated uncertainties in predictions. All three algorithms reproduce the major features of permeability distribution and fluid flow data. For relatively small problems, the Metropolis algorithm is the fastest. For larger problems, the heat-bath algorithm is at least as fast and often faster than the Metropolis algorithm, with significant potential for parallelization. The performance of the genetic algorithm is highly dependent on the choice of population size and probabilities of crossover, update, and mutation.