Reduce Problem Complexity Research Articles

Two-stage dynamic real-time optimization framework using parameter-dependent differential dynamic programming

The purpose of chemical process control includes proactive adjustment of the operation to make the most profit out of it. Within this context, real-time optimization (RTO) is proposed and extended to dynamic RTO (DRTO) in the hierarchical control structure, usually having model predictive control (MPC) below. However, online tractability confined the model complexity of RTO and MPC, which results in model inconsistency and, even, incompatible solutions. Here we use parameter-dependent differential dynamic programming (PDDP) to incorporate the closed-loop behavior of the controller in an RTO layer to reduce problem complexity and online computation time. The adaptive control performance of PDDP and the efficacy of closed-loop DRTO formulation with PDDP is demonstrated with the reaction–storage–separation network system control. Consequently, PDDP provides a useful parameterization method to express closed-loop system dynamics, which enables fast feedback control and integrated plant optimization.

Signal temporal logic synthesis under Model Predictive Control: A low complexity approach

In this paper, we focus on the challenging problem of model predictive control (MPC) for dynamics systems with high-level tasks formulated as signal temporal logic (STL). The state-of-art for STL synthesis mainly suffers from limited scalability with respect to the complexity of the task and the planning horizon, hindering the real-time implementation of MPC. This work tackles this issue by STL formula reformulation and input blocking. Specifically, simplifications are applied on disjunctive STL (sub)formulae recursively in the framework of MPC to limit formula size. We show that the simplified STL can be reformulated into mixed integer linear programming (MILP) constraints with a modifiable number of binary variables being required. The move blocking scheme is then employed to further reduce problem complexity by fixing input variables to be constant over several time intervals. In order to trade off the control performance and computational load, a blocking structure design with on-line correction is proposed. The extension of the proposed STL-MPC algorithm to uncertain systems is achieved through STL constraint tightening. Simulations and experiments show the effectiveness of the proposed algorithm.

Data collection of multi-player cooperative game based on edge computing in mobile crowd sensing

As the number and type of mobile crowd sensing (MCS) data collection increases, more and more computation and processing are required, resulting in higher service cost and service delay, posing a huge challenge to traditional MCS. Currently, edge computing is being introduced to MCS to collect data to reduce service cost and service latency. In the offline mode, there are two issues that need to be addressed with the edge computing-based MCS data collection. First, for large-scale, multi-player, and multiple types of task data, edge servers are limited in computational resources and need to address issues such as task offloading and service cache scheduling. Second, in traditional MCS data collection, workers usually carry in a single type of task data, but it has now been proposed that workers need to carry multiple types of task data. To address the above problems, this paper proposes a joint optimization strategy for edge computing based on multi-player cooperative game and greedy differential evolution algorithm (MCG-GDE) to improve the service rate of edge servers and minimize the service cost and service latency in the data collection. We build a mathematical optimization problem for edge computing based on MCS data collection. The formulation of the optimization problem proves to be a NP-hard problem, so this optimization strategy constructs a task propagation scheme for multi-player cooperative games (MCG), where tasks carried by workers are reassigned to effectively reduce problem complexity and produce sub-optimal solutions to the mathematical model. Then, on the basis of the suboptimal solution, the optimal solution of the problem is obtained by the greedy differential evolution algorithm (GDE). Simulation results demonstrate that MCG-GDE outperforms other baseline strategies.

Optimal heuristic economic management strategy for microgrids based PEM fuel cells

This paper provides a cost-effective energy management method for the microgrid's standalone and on-grid operational modes. The considered microgrid based on Green-to-Green systems such as a photovoltaic array (PV), wind turbine (WT) as renewable generators, hydrogen fuel cell (FC) system, microturbine (MT), and battery storage system. The suggested technique is based on the marine predator algorithm (MPA) that and created for a scheduling horizon of one day. The main objective of this paper is to reduce operating costs while satisfying the demand power. To approve the performance of the suggested management strategy, the simulation results are compared with other recent optimization algorithms. These algorithms include particle swarm optimization (PSO), salp swarm algorithm (SSA), and coot optimization algorithm (COOT). To approve the performance of the proposed EMS, the system will be evaluated for three cases: free renewable generation, restricted generation mode, and unlimited main grid power mode. For the first case, the proposed EMS reduces the operating costs by 0.8732%. For case 2, the economic benefits have been raised by 1.0815% and by 0% for the last case because of the reduced problem complexity.

A Column generation approach for product targeting optimisation within the banking industry ORiON: Product Targeting Optimisation

Product targeting optimisation within the financial sector is becoming increasingly complex as optimisation models are being exposed to an abundance of data-driven analytics and insights generated from a host of customer interactions, statistical and machine learning models as well as new operational, business, and channel requirements. However, given the expeditious change in the data environment, it is evident that the product targeting formulation cited throughout the literature has not yet been updated to align with the realistic modeling dynamics required by financial institutions. In this paper, an enhanced product targeting formulation is proposed that incorporates a large set of new modeling constraints and input parameters to try and maximise the economic profit generated by a financial institution. The proposed formulation ensures that the correct product is offered to the desired customers at the best time of day through their preferred communication medium. To solve the foregoing product targeting formulation, a novel column generation approach is presented that is capable of reducing problem complexity and in turn allowing for significantly larger problems to be solved to global optimality within a reasonable time frame.

Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

Renewable energy sources (RES) such as wind turbines (WT) and solar cells have attracted the attention of power system operators and users alike, thanks to their lack of environmental pollution, independence of fossil fuels, and meager marginal costs. With the introduction of RES, challenges have faced the unit commitment (UC) problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’ inputs and outputs, and specifying the optimal generation of each unit. The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty. As a result, the UC problem in the presence of RES faces uncertainties. The grid consumed load is not always equal to and is randomly different from the predicted values, which also contributes to uncertainty in solving the aforementioned problem. The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem. The new model is adopted to solve the wind-generated power uncertainty, and energy storage systems (ESSs) are included in the problem for further management. The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units. To solve the UC programming model, a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

Integrated planning of grids and energy conversion units in municipal multi-energy carrier systems

The ongoing energy transition requires the planning of low-emission municipal energy supply systems. These systems comprise distribution grids for electricity, gas, and heat, as well as energy conversion units such as heating systems. This paper presents a linear optimization model considering these elements in order to identify the cost-minimizing system design while achieving a given CO2 emission limit. The model is applied to an exemplary test case comprising 900 buildings. In order to increase scalability of the model to larger system sizes, the effect of reducing the spatial resolution on the optimization results is analyzed. The results show that the effect is small and that spatial aggregation is indeed a valid approach to reduce problem complexity and to allow significant speedups, reaching a factor of 200 for the given case study.

Employing Several Kinds of Demand Response Programs to Optimal Eco Emission Planning of Microgrid

Numerous investigations have been carried out to evaluate microgrid optimal configuration. The main goal of this research is evaluating the positive effect of private investor that contributes in the MG configuration in the planning horizon. The MG operator and private investors are the owners of combined heat and power and intelligent parking lot, respectively. The optimization problem was modeled as a two-objective function. For comprehensive evaluation of the planning problem, reliability indices are considered as problem constraints. To show the effects of demand side management in reducing operational costs, several types of demand response programs are used. In the first stage, a bi-objective function is converted to a single objective function by utilizing the weighted sum technique and in the second stage, the best solution is chosen among all Pareto solutions employing max–min fuzzy decision-making method. To reduce problem complexity, SCENRED tools of GAMS software are used to reduce the number of scenarios. The results revealed that in TOU-based DRP, the total cost of MGO and private investor are reduced by 4.28%, 2.67%, respectively.

Optimal and near-optimal alpha-fair resource allocation algorithms based on traffic demand predictions for optical network planning

We examine proactive network optimization in reconfigurable optical networks based on traffic predictions. Specifically, the fair spectrum allocation (SA) problem is examined for a priori reserving resources aiming to achieve near-even minimum quality-of-service (QoS) guarantees for all contending connections. The fairness problem arises when greedy SA policies are followed, which are based on point-based maximum demand predictions, especially under congested networks, with some connections highly overprovisioned and others entirely blocked, resulting in highly uneven QoS connection guarantees. To address this problem, we consider predictive traffic distributions allowing the exploration of several combinations of possible SAs. To find a fair SA policy, we resort to an α -fairness scheme, while QoS fairness is evaluated according to a game-theoretic analysis based on the coefficient of variations of the connections’ unserved traffic metric, which measures the dispersion of unserved traffic for a connection around the mean of the unserved traffic over all connections. Non-contending (NC) and link-based contending (LBC) α -fair SA integer linear programming algorithms are proposed, where the optimal NC approach encompasses global network contention information, while the near-optimal LBC approach encompasses partial contention information to reduce problem complexity. We show that as parameter α increases, QoS fairness improves, along with connection blocking, resource utilization, and overprovisioning and underprovisioning. Further, LBC exhibits results close to the optimal, with a significant improvement in processing time.

On the superiority of modularity-based clustering for determining placement-relevant clusters

In advanced technology nodes, IC implementation faces increasing design complexity as well as ever-more demanding design schedule requirements. This raises the need for new decomposition approaches that can help reduce problem complexity, in conjunction with new predictive methodologies that can help avoid bottlenecks and loops in the physical implementation flow. Notably, with modern design methodologies it would be very valuable to better predict final placement of the gate-level netlist: this would enable more accurate early assessment of performance, congestion and floorplan viability in the SOC floorplanning/RTL planning stages of design. In this work, we study a new criterion for the classic challenge of VLSI netlist clustering: how well netlist clusters “stay together” through final implementation. We propose the use of several evaluators of this criterion. We also explore the use of modularity-driven clustering to identify natural clusters in a given graph without the tuning of parameters and size balance constraints typically required by VLSI CAD partitioning methods. We find that the netlist hypergraph-to-graph mapping can significantly affect quality of results, and we experimentally identify an effective recipe for weighting that also comprehends topological proximity to I/Os. Further, we empirically demonstrate that modularity-based clustering achieves better correlation to actual netlist placements than traditional VLSI CAD methods (our method is also 2× faster than use of hMetis for our largest testcases). Finally, we propose a flow with fast “blob placement” of clusters. The “blob placement” is used as a seed for a global placement tool that performs placement of the flat netlist. With this flow we achieve 20% speedup on the placement of a netlist with 4.9 M instances with less than 3% difference in routed wirelength.

Search-space Decomposition for System-level Design Space Exploration of Embedded Systems

The development of large-scale multi- and many-core platforms and the rising complexity of embedded applications have led to a significant increase in the number of implementation possibilities for a single application. Furthermore, rising demands on safe, energy-efficient, or real-time capable application execution make the problem of determining feasible implementations that are optimal with respect to such design objectives even more of a challenge. State-of-the-art Design Space Exploration (DSE) techniques for this problem demonstrably suffer from the vast and sparse search spaces posed by modern embedded systems, emphasizing the need for novel design methodologies in this field. Based on the idea of reducing problem complexity by a suitable decomposition of the system specification—in particular, by a reduction of target architecture or task mapping options—the work at hand proposes a portfolio of dynamic decomposition mechanisms that automatically decompose any system specification based on a short pre-exploration of the complete system. We present a two-phase approach consisting of (a) a set of novel data extraction and representation techniques combined with (b) a selection of filtering operations that automatically extract a decomposed system specification based on information gathered during pre-exploration. In particular, we employ heat map data structures and threshold as well as graph-partitioning filters to reduce problem complexity. The proposed decomposition procedure can seamlessly be integrated in any DSE flow, constituting a flexible extension for existing DSE approaches. Furthermore, it improves existing static decomposition techniques and other heuristics relying on information about the problem instance, since systems with irregular architectural topology or distribution of resource types can now be decomposed based on an automatic, problem-independent pre-exploration phase. We illustrate the efficiency of the proposed decomposition portfolio applied to state-of-the-art DSEs for many-core systems as well as networked embedded systems from the automotive domain. Experimental results show significant increases in optimization quality of up to 87% within constant DSE time compared to existing approaches.

Application of A-Star Algorithm in Robot for Catering Service Based on Laser Radar Navigation

In order to improve performance of robot autonomous job completion, soft and hard architecture of robot for catering service is designed by laser radar based on ROS system in this paper. And method of general planning path is provided for robot for catering service by A-Star Algorithm. Based on established grid map, mobile path of mobile robot is obtained by continuous search for paths approaching to destination, so as to narrow search area and reduce problem complexity. Paths of service robot under static and dynamic obstacles are tested, which finds that robot can avoid obstacles effectively through coordination with laser radar as well as one optimized path can be planned. Such experiments show that A-Star Algorithm testified in robot for catering service path planning has robustness and efficiency.

Mirroring quasi-symmetric organ observations for reducing problem complexity

Abstract Following an obvious growth of available collections of medical images in recent years, both in number and in size, machine learning has nowadays become an important tool for solving various image-analysis-related problems, such as organ segmentation or injury/pathology detection. The potential of learning algorithms to produce models having good generalisation properties is highly dependent on model complexity and the amount of available data. Bearing in mind that complex concepts require the use of complex models, it is of paramount importance to mitigate representation complexity, where possible, therefore enabling the utilisation of simpler models for performing the same task. When dealing with image collections of quasi-symmetric organs, or imaging observations of organs taken from different quasi-symmetric perspectives, one way of reducing representation complexity would be aligning all the images in a collection for left-right or front-rear orientation. That way, a learning algorithm would not be dealing with learning redundant symmetric representations. In this paper, we study in detail the influence of such within-class variation on model complexity, and present a possible solution, that can be applied to medical-imaging computer-aided diagnosis systems. The proposed method involves compacting the data, extracting features and then learning to separate the mirror-image representation classes from one another. Two efficient approaches are considered for performing such orientation separation: a fully automated unsupervised approach and a semi-automated supervised approach. Both solutions are directly applicable to imaging data. Method performance is illustrated on two 2D and one 3D real-world publicly-available medical datasets, concerning different parts of human anatomy, and observed using different imaging techniques: colour fundus photography, mammography CT scans and volumetric knee-joint MR scans. Experimental results suggest that efficient organ-mirroring orientation-classifier models, having expected classification accuracy greater than 99%, can be estimated using either the unsupervised or the supervised approach. In the presence of noise, however, an equally good performance can be achieved only by using the supervised approach, learning from a small subset of labelled data.

Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances

State estimation and control approaches in electric distribution grids rely on precise electric models that may be inaccurate. This work presents a novel method of estimating distribution line parameters using only root mean square voltage and power measurements under consideration of measurement tolerances, noise, and asynchronous timestamps. A measurement tolerance compensation model and an alternative representation of the power flow equations without voltage phase angles are introduced. The line parameters are obtained using numeric methods. The simulation demonstrates in case of the series conductance that the absolute compensated error is $-1.05\%$ and $-1.07\%$ for both representations, as opposed to the expected uncompensated error of $-79.68\%$ . Identification of a laboratory distribution line using real measurement data grid yields a deviation of 6.75% and 4.00%, respectively, from a calculation based on the manufacturer’s cable specifications and estimated line length. The transformed power flow equations deliver similar results despite the reduced problem complexity.

An Unambiguous Distance-Based MIQP Model to Solve Economic Dispatch Problems with Disjoint Operating Zones

This letter presents an unambiguous distance-based mixed-integer quadratic programming (UDB-MIQP) model to solve economic dispatch problems with disjoint operating zones. By considering the distances among the prohibited zones, the unnecessary variables will be eliminated and, consequently, a significant decrease, both in terms of variables and constraints, is obtained. The results show that the UDB model compared to existing MIQP models in the literature can achieve the global optimal solution with a significantly reduced problem complexity, both in terms of variables and constraints. In addition, the results indicate the superiority of the method in computational time reduction.

A Research on Measuring and Reducing Problem Complexity to Increase System Affordability: From Theory to Practice

Requirement engineering is the cornerstone of systems engineering. Numerous large scale engineered systems face schedule delays, cost overruns and performance shortfalls that can be traced back to the requirements they need to fulfill. In fact, previous research has demonstrated strong relationship between requirements and systems affordability. This paper summarizes and puts into context the authors’ novel contributions in three domains of requirements engineering: systems theory, complexity science, and systems methodologies. The authors propose new theorems and their proofs on requirements affecting affordability, propose a new complexity metric at requirement stage that measures the complexity limit of the system at conceptual stage (even before a specific design is determined), and propose two methodologies to elicit excess-free requirement sets and to identify conflicting requirements more effectively. The paper showcases the value of structuring a research in such a manner, i.e. from theory to practice, enabling strengthening the bounds between theorists and practitioners.

Big-M Based MIQP Method for Economic Dispatch With Disjoint Prohibited Zones

This paper presents a novel big-M based mixed integer quadratic programming (MIQP) method to solve economic load dispatch problem with disjoint prohibited zones. By adding artificial 0-1 binary variables for each prohibited operating zone of generators and employing a binary coding scheme, disjoint feasible regions are represented by complementary linear constraints. Compared to existing MIQP method, the proposed method can achieve global optimal solution with much reduced problem complexity.

Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures.

The extraction of significant structures in arbitrary high-dimensional data sets is a challenging task. Moreover, classifying data points as noise in order to reduce a data set bears special relevance for many application domains. Standard methods such as clustering serve to reduce problem complexity by providing the user with classes of similar entities. However, they usually do not highlight relations between different entities and require a stopping criterion, e.g. the number of clusters to be detected. In this paper, we present a visualization pipeline based on recent advancements in algebraic topology. More precisely, we employ methods from persistent homology that enable topological data analysis on high-dimensional data sets. Our pipeline inherently copes with noisy data and data sets of arbitrary dimensions. It extracts central structures of a data set in a hierarchical manner by using a persistence-based filtering algorithm that is theoretically well-founded. We furthermore introduce persistence rings, a novel visualization technique for a class of topological features-the persistence intervals-of large data sets. Persistence rings provide a unique topological signature of a data set, which helps in recognizing similarities. In addition, we provide interactive visualization techniques that assist the user in evaluating the parameter space of our method in order to extract relevant structures. We describe and evaluate our analysis pipeline by means of two very distinct classes of data sets: First, a class of synthetic data sets containing topological objects is employed to highlight the interaction capabilities of our method. Second, in order to affirm the utility of our technique, we analyse a class of high-dimensional real-world data sets arising from current research in cultural heritage.

Combinatorial complexity problem reduction by the use of artificial vaccines

A general method to reduce computing time for large combinatorial optimization problems by the use of a novel proposal is presented. It is based on reducing the problem complexity by the systematic application of vaccines, it is inspired in the concept of immunization derived from Artificial Immune Systems. The method can be applied practically to any combinatorial problem program solver such as genetic algorithms, memetic algorithms, artificial immune systems, ant colony optimization, the Dantzig–Fulkerson–Johnson algorithm, etc., providing optimal and suboptimal routes outperforming the selected algorithm itself. As a direct consequence of reducing problem complexity, the method provides a means to bring combinatorial optimization open problems that are too big to be treated by known techniques to a tractable point where acceptable solutions can be obtained. To demonstrate the proposed methodology the Traveling Salesman Problem for huge quantity of cities was used, we tested the method with modern evolutionary algorithms and the Concorde program. Comparative experiments that shows the effectiveness of the method are presented.

Can the isolated-elements strategy be improved by targeting points of high cognitive load for additional practice?

Reducing problem complexity by isolating elements has been shown to be an effective instructional strategy. Novices, in particular, benefit from learning from worked examples that contain partially interacting elements rather than worked examples that provide full interacting elements. This study investigated whether the isolating-elements strategy could be improved further by targeting points of high cognitive load for additional practice. In learning to solve algebraic problems, fifty-four 13–14 year-old students were randomly assigned to one of three learning strategies: (a) an isolated-elements strategy with targeted extra practice on key components, (b) an isolated-elements strategy with an equal amount of practice on each component, and (c) a full-worked example strategy without isolating elements. Results showed that the targeted strategy was superior to the full-worked example strategy on a number of measures, but not to the equal practice strategy. For low prior-knowledge learners the equal practice isolated format was superior.