Single Optimization Algorithm Research Articles

A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.

Landing route planning method for micro drones based on hybrid optimization algorithm

Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone, this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method. The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms. In the process of landing route planning, the planning plane is first obtained by making the gliding plane tangent to the obstacle. In the planning plane, the projection of obstacle is transformed into multiple unreachable line segments. By designing an optimization model, the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem. Taking the shortest route as the optimization objective, the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle. During the optimization process, through the orthogonal learning of the intermediate iterative results, the hybrid algorithm can adaptively select the next iterative algorithm, so it can give full play to the respective advantages of the two algorithms. The optimization results show that, compared with the single optimization algorithm, the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.

Joint Calibration for DTA Model Using Islands-GA and PC-SPSA

Dynamic Traffic Assignment (DTA) models are widely used in transportation system management. Calibration is a crucial step to improve the reliability and the accuracy of DTA models. We present a systematic framework to offline calibrate the supply and demand component of a DTA model. The essence of model calibration is an optimization problem, aiming to minimize the discrepancy between field conditions and simulated traffic measurements. To overcome limitations of a single optimization algorithm, a joint approach is developed for the calibration of supply and demand component respectively with different traffic measurements. As the calibration process is a nonlinear and stochastic problem, heuristic algorithms: the Genetic Algorithm (GA) and the Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm, are implemented as a complement solution. Instead of using the standard GA, to expedite searching efficiency, we introduce the Islands Genetic Algorithm (IGA) and SPSA with Principal Component Analysis (PC-SPSA) to solve the calibration problem. A case study on a network of Munich, Germany, is used to validate the proposed methodology. The promising results indicate that calibration of the supply and demand component of a DTA model with the proposed joint approach improves modelling accuracy. In comparison, IGA outperforms standard GA in terms of convergence speed and solution quality.

COVID-19: coagulation disorders and anticoagulant treatment in patients hospitalised in ICU.

Patients hospitalized in the intensive care unit (ICU) due to the COVID-19 experience ahigh incidence (up to 43%) of venous thromboembolic events. While laboratory findings in COVID-19-associated coagulopathy (CAC) show increased D-dimer and fibrinogen levels, the abnormalities in standard coagulation tests and platelet count are minimal. Recent studies suggest contribution of fibrinolysis shutdown to this phenomenon. Endothelial injury and alteration of its antithrombotic activity can lead to micro- and macrovascular thrombosis in the lungs, occurrence of which is associated with poor clinical outcome in critically ill patients with COVID-19. Additionally, the hypercoagulability induced by activation of coagulation pathways during the immune response to SARS-CoV-2 infection contributes to impaired organ perfusion. This, alongside with hypoxemia, leads to multiorgan failure. Various diagnostic regimens, some of which include global assays of haemostasis, are currently being published and discussed. Numerous guidelines and recommendations of scientific societies and groups of specialists have been published. However, there is no single optimal algorithm for anticoagulation treatment and monitoring specific to the ICU patients with COVID-19. The authors have attempted to summarize the data related to CAC and thrombotic disease and develop an algorithm consistent with the latest clinical practice guideline recommendations.

Design of structure and control system of semiconductor refrigeration box

In order to create low temperature environment for the valve testing, a new type of semiconductor refrigeration box based on semiconductor refrigeration chip and programmable logic controller (PLC) control system is designed. The power of the semiconductor refrigeration chip is determined by calculating the heat dissipation characteristics of the semiconductor refrigeration box. Combining natural convection heat dissipation with forced air cooling, the heat sink of semiconductor refrigeration chip is designed. In the control strategy, switch control is combined with an intelligent control strategy. Adaptive single neuron optimization algorithm based on quadratic optimization is adopted to adjust and optimize the parameters of the proportional-integral-derivative (PID) controllers in real time. Taking into account the limited hardware capabilities of the PLC, the Jacobian information in parameter adjustment is redesigned into a simplified form of identification. The actual test results of refrigeration box show good control performance.

Algorithm Selection for Optimal Multi-Agent Pathfinding

The challenge of finding an optimal solution to a multi-agent path finding (MAPF) problem has attracted significant academic and industrial interest in recent years. While the problem is NP-Hard, modern optimal MAPF algorithms can scale to solve problems with hundreds of agents. Nevertheless, no single optimal MAPF algorithm dominates all benchmarks problems, and there are no clear, provable, guidelines for when each algorithm should be used. To address this, we present the first successful Algorithm Selection (AS) model for optimal MAPF. We propose two approaches for learning an AS model. The first approach uses a standard supervised learning algorithm with a set of handcrafted MAPF-specific features. The second approach, casts a MAPF problem to an image and applies a deep Convolutional Neural Network to classify it. We evaluate both approaches over a large dataset and show that using an AS model to select which algorithm to use for each instance results in solving more problems and in a shorter runtime compared to the state of the art.

A hybrid strategy on combining different optimization algorithms for hazardous gas source term estimation in field cases

Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency.

Firefly algorithm based on intelligent single particle learning

The particles in the population of the firefly algorithm learn from each other using an all-attractive model, and the algorithm has a strong ability of social learning and global detection. However, the algorithm ignores the role of the global optimal particle, resulting in weak self-learning and local development ability of the algorithm. Therefore, this paper proposes an intelligent single-particle learning firefly algorithm. The algorithm divides the iterative process into two stages, the first stage adopts the standard firefly algorithm to evolve; in the second stage, the intelligent single particle optimisation algorithm is used to optimise the global optimal particle. The iterative process in the first stage ensures the sociality and global detection ability of the particle, and the second stage enhances the ability of self-learning and local development of the algorithm. The experimental results show that the algorithm in this paper has better performance.

Coupling matrix synthesis of general chebyshev filters

A single optimization algorithm based on SolvOpt that synthesizes coupling matrices for cross-coupled microwave filters is presented. The rules for setting initial values of SolvOpt are proposed to find global minimum of the cost function. SolvOpt method provides faster convergence and higher accuracy to find the final solution compared with hybrid optimization algorithms. Application examples illustrate the excellent performance and the validity of this method.

Design of an Helical Spring using Single-solution Simulated Kalman Filter Optimizer


 
 
 
 Optimization is one of the important process in solving engineering problems. Regrettably, there are numerous problems in practical optimization that cannot be solved flawlessly within reasonable computational effort. Thus, metaheuristic approach is often useful to get near-optimal solution when the best solution is not achievable. This paper demonstrates the usefullness of a metaheuristic algorithm called single-solution simulated Kalman filter (ssSKF) in helical spring design, which is an example of structural engineering design problem. The ssSKF is a single agent-based optimization algorithm based on the Kalman filtering. The solution obtained by the ssSKF is compared againsts the genetic algorithm, co-evolutionary particle swarm optimization, co-evolutionary differential evolution, bat algorithm, and artificial bee colony.
 
 
 

Single and Multi-Objective Optimization Algorithms

Single and Multi-Objective Optimization Algorithms

A novel combined model based on hybrid optimization algorithm for electrical load forecasting

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people’s daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to sub-optimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning.

Fuzzy optimization design-based multi-level response surface of bogie frame

Purpose In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem. Design/methodology/approach The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination. Findings The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively. Originality/value Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

Suspended sediment load prediction using non-dominated sorting genetic algorithm II

Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management. Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it, the results of this model are still not sufficiently accurate. In this study, in order to increase the efficiency of SRC model, a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River, Iran are used as a case study. In the first part of the study, using self-organizing map (SOM), an unsupervised artificial neural network, the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models, respectively. In the second part of the study, two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated. The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-II algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study. Given that the use of the SRC model is common, the proposed model in this study can increase the efficiency of this regression model.

Optimal planning of battery systems for power losses reduction in distribution grids

The minimization of the power losses in the distribution grid is one of the main issue for the Distribution System Operator in order to reduce the management costs of the grid. The recent development of the battery technologies has introduced new possible applications for these systems within the radial distribution grid. In fact, batteries can be opportunely integrated in the grid and managed in order to reduce the power losses, and consequently increasing, for instance, the penetration of the renewable distributed generation and contributing to voltage regulation through reactive power production from battery inverter. In this context, one of the main research interest is the definition of the optimal siting and sizing of the energy storage solutions, considering a battery management capable to reduce network power losses and taking into account battery installation costs.Since network losses are expressed by means of quadratic function, an approach based on Mixed Integer Quadratically Constrained Quadratic Programming is proposed here to identify possible batteries optimal management strategies capable to minimize the power losses. Evaluation of the grid variables is obtained by the iterative Backward/Forward Sweep method implemented within the formulation of the optimization problem. The reactive power generated by the battery inverter is modeled as well by introducing quadratic constraints in order to further contribute in the network power losses reduction, taking into account the power factor limitation due to the inverter capability curve.An optimization procedure, called D-XEMS13, based on the formulation of the optimization problem, is implemented within a single loop optimization algorithm in order to identify the best size of the connected BESSs units, capable to maximize the reduction of the power losses in test grids. BESS optimal placement is also identified through a proposed approach based on a nodal sensitivity analysis of the network power losses. Validation of the proposed approach is then obtained by means of a cost/benefit comparison from energy point of view.Finally, the single loop optimization algorithm is further implemented on two test grids with optimal BESSs sizes and siting, assuming different capability curves for the BESS inverters. The results of simulations with the corresponding benefits are then presented and discussed.

Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network.

The growing and pruning radial basis function (GAP-RBF) network is a promising sequential learning algorithm for prediction analysis, but the parameter selection of such a network is usually a non-convex problem and makes it difficult to handle. In this paper, a hybrid bioinspired intelligent algorithm is proposed to optimize GAP-RBF. Specifically, the excellent local convergence of particle swarm optimization (PSO) and the extensive search ability of genetic algorithm (GA) are both considered to optimize the weights and bias term of GAP-RBF. Meanwhile, a competitive mechanism is proposed to make the hybrid algorithm choose the appropriate individuals for effective search and further improve its optimization ability. Moreover, a decoupled extended Kalman filter (DEKF) method is introduced in this study to reduce the size of error covariance matrix and decrease the computational complexity for performing real-time predictions. In the experiments, three classic forecasting issues including abalone age, Boston house price and auto MPG are adopted for extensive test, and the experimental results show that our method performs better than PSO and GA these two single bioinspired optimization algorithms. What is more, our method via DEKF achieves the better results in comparison with the state-of-art sequential learning algorithms, such as GAP-RBF, minimal resource allocation network, resource allocation network using an extended Kalman filter and resource allocation network.

A sequential algorithm portfolio approach for black box optimization

Abstract A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.

Optimization of a hybrid system for solar-wind-based water desalination by reverse osmosis: Comparison of approaches

Nineteen evolutionary algorithms, including single and hybrid optimization algorithms, are used for determining the optimum size of a hybrid renewable energy system (HRES) that is comprised of a wind turbine, a photovoltaic panel, a battery bank, and a reverse osmosis desalination unit. The main source electrical power for the reverse-osmosis desalination unit for producing fresh water is solar and wind energy, and batteries are used as back up units. Integer and continuous variables in the HRES optimization model for a remote area of Iran are considered. The optimization aims to meet the load continuously while minimizing the HRES life cycle cost subject to relevant constraints. Also, to ensure reliability, the reliability index is assessed for the loss of power supply probability. In order to achieve optimal performance, various versions well-known optimization approaches are used: particle swarm optimization, bee swarm optimization, harmony search, simulated annealing, chaotic search, and tabu search algorithm. The results show that hybrid optimization techniques provide the best performance among the considered evolutionary algorithms and that using the HRES reduces system costs and increases system reliability in general and for increasing fresh water availability.

A Multiagent-Based Hierarchical Energy Management Strategy for Multi-Microgrids Considering Adjustable Power and Demand Response

Conventionally, community energy management system (CEMS) is provided with the information of surplus and shortage amounts only at each time interval. This limited information may lead to an increase in the operational cost of the multimicrogrid (MMG) systems. This paper suggests informing the CEMS about the adjustable power also, in addition to the surplus and shortage information. This additional information will result in a variety of options for the CEMS to fulfill the load demands of its network. CEMS will choose among various available options, which include trading with the power grid, buying from a controllable distributed generation plant, buying from a community battery energy storage system (CBESS), or controlling the adjustable power: increasing or decreasing the generation of controllable units. CBESS can either be controlled by CEMS or can act as an autonomous entity. The effects of both the operational options have been analyzed and economically efficient mode is suggested for MMG systems. Demand response (DR) is also considered in the proposed model. The incorporation of DR will ensure the supply reliability of the MMG system in addition to the reduction in operational cost. In contrast to the conventional single or two-step multimicrogrid optimization algorithms, a multistep hierarchical optimization algorithm based on a multiagent system is proposed in this paper. Easy to implement and computationally inexpensive mixed integer linear programming models are developed for each step.

Economic and Emission Dispatch Using Ensemble Multi-Objective Differential Evolution Algorithm

In the past two decades, China’s manufacturing industry has achieved great success. However, pollution and environmental impacts have become more serious while this industry has grown. The economic and emission dispatch (EED) problem is a typical multi-objective optimization problem with conflicting fuel costs and pollution emission objectives. An ensemble multi-objective differential evolution (EMODE) is proposed to tackle the EED problem. First, the equality constraints of the problem have been transformed into inequality constraints. Next, two mutation strategies DE/rand/1 and DE/current-to-rand/1 have been implemented to improve the conventional DE. The performance of the proposed algorithm is evaluated on six test functions and the numerical results have indicated that the proposed algorithm is effective. The proposed algorithm EMODE is used to solve a series of six generators and eleven generators in the EED problem. The experimental results obtained are compared with those reported using single optimization algorithms and multi-objective evolutionary algorithms (MOEAs). The results have revealed that the proposed algorithm EMODE either matches or outperforms those algorithms. The proposed algorithm is an effective candidate to optimize the manufacturing industry of China.