Set Of Candidate Solutions Research Articles

Detailed evaluation of a population-wise personalization approach to generate synthetic myocardial infarct images

Personalization of biophysical models to real data is essential to achieve realistic simulations or generate relevant synthetic populations. However, some of these models involve randomness, which poses two challenges: they do not allow the standard personalization to each individual’s data and they lack an analytical formulation required for optimization. In previous work, we introduced a population-based personalization strategy which overcomes these challenges and demonstrated its feasibility on simple 2D geometrical models of myocardial infarct. The method consists in matching the distributions of the synthetic and real populations, quantified through the Kullback–Leibler (KL) divergence. Personalization is achieved with a gradient-free algorithm (CMA-ES), which generates sets of candidate solutions represented by their covariance matrix, whose coefficients evolve until the synthetic and real data are matched. However, the robustness of this strategy regarding settings and more complex data was not challenged. In this work, we specifically address these points, with (i) an improved design, (ii) a thorough evaluation on crucial aspects of the personalization process, including hyperparameters and initialization, and (iii) the application to 3D data. Despite some limits of the simple geometrical models used, our method is able to capture the main characteristics of the real data, as demonstrated both on 2D and 3D segmented late Gadolinium images of 123 subjects with acute myocardial infarction.

A Performance Indicator-Based Infill Criterion for Expensive Multi-/Many-Objective Optimization

In surrogate-assisted multi-/many-objective evolutionary optimization, each solution normally has an approximated value on each objective, resulting in increased difficulties in selecting solutions for expensive objective evaluations due to complicated trade-off between different objectives and accumulated uncertainty in the approximation of the objective functions. Thus, it is highly challenging to design an efficient model management strategy for surrogate-assisted expensive multi-/many-objective optimization. In this paper, a surrogate model is built for each objective function, based on which a set of promising candidate solutions are found. Additionally, a Gaussian process model is constructed to approximate a newly designed performance indicator measuring both convergence and diversity properties of individual solutions. Finally, the solution of the found candidate solutions having the maximum expected improvement in terms of the performance indicator is selected for evaluation using the expensive objective functions. Comparative experiments are conducted on 3-, 5-, and 10-objective DTLZ, WFG, and MaF test functions, as well as two real-world applications. The experimental results show that the proposed method is competitive compared to five state-of-the-art surrogate-assisted evolutionary algorithms for expensive multi-/many-objective optimization.

Vanadium redox flow batteries real-time State of Charge and State of Health estimation under electrolyte imbalance condition

This paper presents a novel observer architecture capable to estimate online the concentrations of the four vanadium species present in a vanadium redox flow battery (VRFB). The proposed architecture comprises three main stages: (1) a high-gain observer, to estimate the output voltage and its derivatives; (2) a dynamic inverter, to obtain a set of concentration candidate solutions; and (3) a static selector, to determine the actual concentrations. The methodology does not rely on the classic assumption of balanced electrolytes, thus significantly widening the application range in comparison with most of the literature previous studies. Furthermore, to perform the estimation, only a single voltage and current measurements are required, which eliminates the need of including complex and costly additional sensors.To validate the proposal, comprehensive simulation tests are conducted. These tests take into account typical side reactions that cause imbalance in VRFB systems, such as vanadium crossover and V2+ oxidation. The observer shows a remarkable performance when dealing with these realistic conditions, allowing to estimate with high accuracy and robustness the four vanadium concentrations, the State of Charge and the State of Health with a relative error below 2%.

Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach

Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems.

An intelligent edge-enabled distributed multi-task learning architecture for large-scale IoT-based cyber–physical systems

Internet-of-Things (IoT)-based cyber–physical systems are increasingly being adopted because of the recent technological advancements in sensor technology, edge computing, machine learning, and big data. Integrating machine learning into designing IoT-based cyber–physical systems is essential. However, it is considered a challenging problem. This stems from the fact that IoT devices generate extensive data that requires extensive processing to achieve adequate learning. Relying on local learning by each IoT device is not feasible in most cases due to its limited resources. On the contrary, relying on all cloud-based learning requires transmitting a large amount of data to the cloud to perform the learning process, which is inefficient in large-scale IoT deployments. Therefore, this paper proposes a novel edge-computing architecture that employs the concept of distributed multi-task learning over EC networks in large-scale IoT-based cyber–physical systems. The architecture develops multiple distributed learning algorithms, a data placement architecture, task allocation algorithms, and a network protocol. In addition, it considers the problem of learning model parameters from IoT data distributed over different edge nodes in a large geographical area without sending raw data to the cloud. The architecture supports several distributed machine models that are trained using a combination of machine learning algorithms and population-based search algorithms to optimize the learning process. Population-based search algorithms allow for maintaining a set of candidate solutions, with each solution corresponding to a unique point in the search space for an optimal solution. Having the dataset distributed over several edge nodes, with each node having its own unique set of candidate solutions, increases the chance of finding a solution that generalizes well for the overall dataset combined. Simulation experiments with real IoT datasets are conducted to evaluate the accuracy of the proposed learning models. Results show the ability to achieve high-accuracy results that are close to single-machine models but with significantly efficient edge computing resource utilization.

Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.

Non-Stationary Stochastic Global Optimization Algorithms

Studying the theoretical properties of optimization algorithms such as genetic algorithms and evolutionary strategies allows us to determine when they are suitable for solving a particular type of optimization problem. Such a study consists of three main steps. The first step is considering such algorithms as Stochastic Global Optimization Algorithms (SGoals ), i.e., iterative algorithm that applies stochastic operations to a set of candidate solutions. The second step is to define a formal characterization of the iterative process in terms of measure theory and define some of such stochastic operations as stationary Markov kernels (defined in terms of transition probabilities that do not change over time). The third step is to characterize non-stationary SGoals, i.e., SGoals having stochastic operations with transition probabilities that may change over time. In this paper, we develop the third step of this study. First, we generalize the sufficient conditions convergence from stationary to non-stationary Markov processes. Second, we introduce the necessary theory to define kernels for arithmetic operations between measurable functions. Third, we develop Markov kernels for some selection and recombination schemes. Finally, we formalize the simulated annealing algorithm and evolutionary strategies using the systematic formal approach.

L1-norm vs. L2-norm fitting in optimizing focal multi-channel tES stimulation: linear and semidefinite programming vs. weighted least squares

Background and Objective: This study focuses on Multi-Channel Transcranial Electrical Stimulation, a non-invasive brain method for stimulating neuronal activity under the influence of low-intensity currents. We introduce a mathematical formulation for finding a current pattern that optimizes an L1-norm fit between a given focal target distribution and volumetric current density inside the brain. L1-norm is well-known to favor well-localized or sparse distributions compared to L2-norm (least-squares) fitted estimates.Methods: We present a linear programming approach that performs L1-norm fitting and penalization of the current pattern (L1L1) to control the number of non-zero currents. The optimizer filters a large set of candidate solutions using a two-stage metaheuristic search from a pre-filtered set of candidates.Results: The numerical simulation results obtained with both 8- and 20-channel electrode montages suggest that our hypothesis on the benefits of L1-norm data fitting is valid. Compared to an L1-norm regularized L2-norm fitting (L1L2) via semidefinite programming and weighted Tikhonov least-squares method (TLS), the L1L1 results were overall preferable for maximizing the focused current density at the target position, and the ratio between focused and nuisance current magnitudes.Conclusions: We propose the metaheuristic L1L1 optimization approach as a potential technique to obtain a well-localized stimulus with a controllable magnitude at a given target position. L1L1 finds a current pattern with a steep contrast between the anodal and cathodal electrodes while suppressing the nuisance currents in the brain, hence, providing a potential alternative to modulate the effects of the stimulation, e.g., the sensation experienced by the subject.

Improved Adaptive Multi-Objective Particle Swarm Optimization of Sensor Layout for Shape Sensing with Inverse Finite Element Method.

The inverse finite element method (iFEM) is one of the most effective deformation reconstruction techniques for shape sensing, which is widely applied in structural health monitoring. The distribution of strain sensors affects the reconstruction accuracy of the structure in iFEM. This paper proposes a method to optimize the layout of sensors rationally. Firstly, this paper constructs a dual-objective model based on the accuracy and robustness indexes. Then, an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm is developed for this model, which introduces initialization strategy, the adaptive inertia weight strategy, the guided particle selection strategy and the external candidate solution (ECS) set maintenance strategy to multi-objective particle swarm optimization (MOPSO). Afterwards, the performance of IAMOPSO is verified by comparing with MOPSO applied on the existing inverse beam model. Finally, the IAMOPSO is employed to the deformation reconstruction of complex plate-beam model. The numerical and experimental results demonstrate that the IAMOPSO is an excellent tool for sensor layout in iFEM.

Machine learning-based framework to cover optimal Pareto-front in many-objective optimization

One of the crucial challenges of solving many-objective optimization problems is uniformly well covering of the Pareto-front (PF). However, many the state-of-the-art optimization algorithms are capable of approximating the shape of many-objective PF by generating a limited number of non-dominated solutions. The exponential increase of the population size is an inefficient strategy that increases the computational complexity of the algorithm dramatically—especially when solving many-objective problems. In this paper, we introduce a machine learning-based framework to cover sparse PF surface which is initially generated by many-objective optimization algorithms; either by classical or meta-heuristic methods. The proposed method, called many-objective reverse mapping (MORM), is based on constructing a learning model on the initial PF set as the training data to reversely map the objective values to corresponding decision variables. Using the trained model, a set of candidate solutions can be generated by a variety of inexpensive generative techniques such as Opposition-based Learning and Latin Hypercube Sampling in both objective and decision spaces. Iteratively generated non-dominated candidate solutions cover the initial PF efficiently with no further need to utilize any optimization algorithm. We validate the proposed framework using a set of well-known many-objective optimization benchmarks and two well-known real-world problems. The coverage of PF is illustrated and numerically compared with the state-of-the-art many-objective algorithms. The statistical tests conducted on comparison measures such as HV, IGD, and the contribution ratio on the built PF reveal that the proposed collaborative framework surpasses the competitors on most of the problems. In addition, MORM covers the PF effectively compared to other methods even with the aid of large population size.

Bound Sets Approach to Impulsive Floquet Problems for Vector Second-Order Differential Inclusions

In this paper, the existence and the localization of a solution of an impulsive vector multivalued second-order Floquet boundary value problem are investigated. The method used in the paper is based on the combination of a fixed point index technique with bound sets approach. At first, problems with upper-Carathéodory right-hand sides are investigated and it is shown afterwards how can the conditions be simplified in more regular case of upper semi-continuous right hand side. In this more regular case, the conditions ensuring the existence and the localization of a solution are put directly on the boundary of the considered bound set. This strict localization of the sufficient conditions is very significant since it allows some solutions to escape from the set of candidate solutions. In both cases, the \(C^1\)-bounding functions with locally Lipschitzian gradients are considered at first and it is shown afterwards how the conditions change in case of \(C^2\)-bounding functions. The paper concludes with an application of obtained results to Liénard-type equations and inclusions and the comparisons of our conclusions with the few results related to impulsive periodic and antiperiodic Liénard equations are obtained.

Customized Analytic Center Cutting Plane Methods for the Discrete-Time Integral Quadratic Constraint Problem

This paper deals with the design of efficient algorithms for solving analysis problems formulated in the integral quadratic constraint (IQC) framework. The oracle for the discrete-time IQC problem is coupled with the analytic center cutting plane method (ACCPM), and the implementation of the ACCPM is customized to leverage promising features of the oracle such as the possibility of generating strictly separating hyper planes between the candidate solution and feasible set and the possibility of generating more than one separating hyper plane. Such customizations may be needed if the ACCPM is to be used as part of recursive IQC synthesis algorithms for practical problems. Numerical examples are generated to compare various versions of the ACCPM, namely, with single center cuts, single deep cuts, and two center cuts.

Smart border patrol using drones and wireless charging system under budget limitation

Harsh environmental factors surrounding the national borders as well as their extensive length make the use of manned systems for monitoring every location on the border a risky, expensive, and hardly possible task. Smart border patrol using small-size drones may provide significant help in patrolling areas inaccessible to patrol agents, reduce agent response time, and increase the safety of patrol agents working in dangerous regions. However, the short flight duration associated with small-size drones due to battery limitation can be a serious drawback for such a system for seamless surveillance. Therefore, this paper proposes a drone-based continuous surveillance system for border patrol with a dynamic wireless battery charging system that is built onto an electrification line (E-line). To control the maximum time interval between two consecutive flights to a particular section of the border, a permitted revisiting gap is assigned to each location on the border based on its criticality. A multi-objective mixed-integer non-linear programming (MINLP) model is developed to minimize both the total length of the E-line system for the wireless charging system and the number of drones to satisfy the revisiting gap constraint for a secure border patrol. A solution algorithm is proposed to achieve the Pareto-optimal set. This method provides the decision-maker with a set of candidate solutions to choose from based on their priorities and budgetary constraints. We illustrate our method using a case study on a segment of the US-Mexico borderline.

An alternative way of evolutionary multimodal optimization: density-based population initialization strategy

Evolutionary algorithms rely on the population initialization strategy to determine a set of candidate solutions, which provide the preliminary knowledge of the problem landscape for the subsequent evolutionary process. However, the distribution of the initial population is rarely concerned in the multimodal evolutionary community. Moreover, a large initial population has not been attractive enough for evolutionary multimodal optimization, because it is difficult and challenging to achieve a balance between diversity and convergence within limited computing resources. As an extended version of our previous conference paper, this paper focuses on the construction of a large initial population that is beneficial for evolutionary multimodal optimization. First, we propose an improved density-based population initialization strategy to generate a uniform initial population with a certain degree of randomness. Then, after acquiring the raw fitness landscape of the problem, a fitness-weighted density-based population initialization strategy is proposed to explore potential global peaks. Finally, we design a multi-species cooperative coevolution algorithm for multimodal optimization, which balances the exploration and exploitation of such a population in the evolutionary process. Experimental results demonstrate that the proposed algorithm is better than the mainstream algorithms on the CEC2013 multimodal optimization benchmark.

Magnetic radial inversion for 3-D source geometry estimation

SUMMARY We present a method for inverting total-field anomaly data to estimate the geometry of a uniformly magnetized 3-D geological source in the subsurface. The method assumes the total-magnetization direction is known. We approximate the source by an ensemble of vertically juxtaposed right prisms, all of them with the same total-magnetization vector and depth extent. The horizontal cross-section of each prism is a polygon defined by a given number of equi-angularly spaced vertices from 0° to 360°, whose polygon vertices are described by polar coordinates with an origin defined by a horizontal location over the top of each prism. Because our method estimates the radii of each polygon vertex we refer to it as radial inversion. The position of these vertices, the horizontal location of each prism and the depth extent of all prisms are the parameters to be estimated by solving a constrained non-linear inverse problem of minimizing a goal function. We run successive inversions for a range of tentative total-magnetization intensities and depths to the top of the shallowest prism. The estimated models producing the lowest values of the goal function form the set of candidate solutions. To obtain stabilized solutions, we impose the zeroth- and first-order Tikhonov regularizations on the shape of the prisms. The method allows estimating the geometry of both vertical and inclined sources, with a constant direction of magnetization, by using the Tikhonov regularization. Tests with synthetic data show that the method can be of utility in estimating the shape of the magnetic source even in the presence of a strong regional field. Results obtained by inverting airborne total-field anomaly data over the Anitápolis alkaline–carbonatitic complex, in southern Brazil, suggest that the emplacement of the magnetic sources was controlled by NW–SE-trending faults at depth, in accordance with known structural features at the study area.

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks.

Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

A random search for discrete robust design optimization of linear-elastic steel frames under interval parametric uncertainty

This study presents a new random search method for solving discrete robust design optimization (RDO) problem of planar linear-elastic steel frames. The optimization problem is formulated with an explicit objective function of discrete design variables, unknown-but-bounded uncertainty in material properties and external loads, and black-box constraint functions of the structural responses. Radial basis function (RBF) models serve as approximations of structural responses. The anti-optimization problem is approximated by an RBF-based optimization problem that is solved using an adaptive strategy coupled with a difference-of-convex functions algorithm. The adaptive strategy is embedded in an iterative process for solving the upper-level optimization problem. This process starts with a set of candidate solutions and iterates through selecting the best candidate among the available candidates and generating new promising candidates by performing a small random perturbation around the best solution found so far to refine the RBF approximations. It terminates when the number of iterations reaches an upper bound, and outputs the optimal solution that is the best solution obtained through the optimization process. Two test problems and two design examples demonstrate that the exact optimal or a good approximate solution can be found by the proposed method with a few trials of the algorithm.

Improved Membrane Algorithm Under the Framework of P Systems to Solve Multimodal Multiobjective Problems

Multimodal multiobjective problems (MMOPs) exist in scientific research and practical projects, and their Pareto solution sets correspond to the same Pareto front. Existing evolutionary algorithms often fall into local optima when solving such problems, which usually leads to insufficient search solutions and their uneven distribution in the Pareto front. In this work, an improved membrane algorithm is proposed for solving MMOPs, which is based on the framework of P system. More specifically, the proposed algorithm employs three elements from P system: object, reaction rule, and membrane structure. The object is implemented by real number coding and represents a candidate solution to the optimization problem to be solved. The function of the reaction rule of the proposed algorithm is similar to the evolution operation of the evolutionary algorithm. It can evolve the object to obtain a better candidate solution set. The membrane structure is the evolutionary logic of the proposed algorithm. It consists of several membranes, each of which is an independent evolutionary unit. This structure is used to maintain the diversity of objects, so that it provides multiple Pareto sets as output. The effectiveness verification study was carried out in simulation experiments. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving all 22 multimodal benchmark test problems in CEC2019.

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach.

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

Towards a sustainable conceptual design of mechatronic systems application to a regenerative braking system

Mechatronic systems use the synergies from the interaction of electronics, mechanics and information technology. They include a wide range of interconnected components using application specific software, which makes their design a multi domain exercise. In contrast to the fact that the improvement of the functionality of the mechatronic system has been the main objective of the design in the past, nowadays a special focus on the environmental aspects is needed in order to create sustainable products. As a matter of fact, a sustainable design methodology for mechatronic systems is needed. In this paper, we propose a sustainable conceptual design methodology for mechatronic systems on the example of a regenerative braking system. Thus, we outline the proposed methodology going from the specifications to defining a set of candidate solutions satisfying requirements including environmental ones by using a new tool which we nominate Design Environmental Matrix. The proposed methodology allows us to considerate environmental aspects in the earlier phase of mechatronics system development process.