Decision Vector Research Articles

Distributed decision-coupled constrained optimization via Proximal-Tracking

In this paper we deal with decision-coupled problems involving multiple agents over a network. Each agent has its own local objective function and local constraints, and all agents aim at finding the value of a common decision vector that minimizes the sum of all agents’ cost functions and satisfies all local constraints. To this purpose, we introduce a Proximal-Tracking distributed optimization algorithm that integrates dynamic average consensus within the proximal minimization method. Convergence to an optimal consensus solution is guaranteed for any value of a constant penalty parameter, under a convexity assumption only, without requiring differentiability, Lipschitz continuity, or smoothness of the local objective functions. Numerical simulations show the effectiveness of the proposed scheme.

Bregman-style Online Convex Optimization with Energy Harvesting Constraints

This paper considers online convex optimization (OCO) problems where decisions are constrained by available energy resources. A key scenario is optimal power control for an energy harvesting device with a finite capacity battery. The goal is to minimize a time-average loss function while keeping the used energy less than what is available. In this setup, the distribution of the randomly arriving harvestable energy (which is assumed to be i.i.d.) is unknown, the current loss function is unknown, and the controller is only informed by the history of past observations. A prior algorithm is known to achieve O(√T) regret by using a battery with an O(√T) capacity. This paper develops a new algorithm that maintains this asymptotic trade-off with the number of time steps T while improving dependency on the dimension of the decision vector from O(√n) to O(√log(n)). The proposed algorithm introduces a separation of the decision vector into amplitude and direction components. It uses two distinct types of Bregman divergence, together with energy queue information, to make decisions for each component.

Generalized Sequential Differential Calculus for Expected-Integral Functionals

Motivated by applications to stochastic programming, we introduce and study the expected-integral functionals, which are mappings given in an integral form depending on two variables, the first a finite dimensional decision vector and the second one an integrable function. The main goal of this paper is to establish sequential versions of Leibniz’s rule for regular subgradients by employing and developing appropriate tools of variational analysis.

A Gene Importance based Evolutionary Algorithm (GIEA) for identifying critical nodes in Cyber–Physical Power Systems

Protecting the critical nodes of a Cyber–Physical Power System (CPPS) is an effective strategy for mitigating the risk of incurring large-scale blackouts. A Gene Importance based Evolutionary Algorithm (GIEA) is proposed to identify a set of critical k nodes by maximizing the total load loss received by end-users. GIEA adopts an importance-based evolutionary strategy to improve the algorithm’s convergence and accuracy, in which the initial node importance metrics are assessed based on dynamic power flows and topology information. Both performance contribution (PC) and coupling failure impact (CFI) are considered in our importance evaluation framework. The impacts of different types of communication nodes on power networks are integrated into the proposed cascading failure model and CFI assessment. Based on the coupling and interdependence information, the strong coupling node pairs are identified to reduce the dimension of the decision vector to improve the computational efficiency of GIEA. The effectiveness and superiority of the proposed methods are illustrated through an example of a coupling CPPS consisting of the IEEE 30-bus model and a communication network with the small-world structure.

MODELING OF MAXIMALLY PARALLEL STRUCTURES OF ALGORITHMS FOR SOLVING THERMAL PROBLEMS

The paper demonstrates the possibility of creating a maximum parallel form of computational algorithms to solve thermal problems and their mapping to the architecture of multiprocessor systems based on solving thermal problems of mathematical physics. It is shown that an effective tool for studying heat and mass transfer problems in metallurgical production could be parallel computing technologies on distributed cluster systems with a relatively low cost and reasonably easily scalable both in the number of processors and in the amount of RAM. Tridiagonal structure systems' parallelization was implemented by a numerical-analytical approach, which predetermined their maximally parallel algorithmic form. That approach is facilitated by the minimum possible implementation time of the developed algorithm on parallel computing systems. Furthermore, during the arithmetic expressions parallel computations, the developed algorithm separates the error in the output data from rounding operations. Thus, the parallelization of tridiagonal systems based on numerical-analytical discretization methods does not impose any restrictions on the topology of the mesh nodes of the computational domain.Furthermore, as applied to the parallel computation of arithmetic expressions, it separates the initial data error from a real PC's rounding operations. That approach eliminates the recurrent structure of computing the sought-for decision vectors, which, as a rule, leads to the round-off errors accumulation. Such a parallel form of the constructed algorithm is maximal and has the shortest possible implementation time of the algorithm on parallel computing systems. The developed approach to parallelizing the mathematical model is stable for various types of input data. It has the most parallel form and is distinguished by the minimum time for solving the problem as applied to multiprocessor computing systems. That is explained as follows. If it is hypothesized that one processor can be assigned to one processor and one processor can be assigned to one node of the computational mesh domain, the computations can be processed in parallel and simultaneously for all nodes of the computational mesh domain. The simulation process was implemented on a PC cluster. It follows from the simulation results analysis that the developed method for solving the heat conduction problem effectively minimizes residuals.

A Two-Stage Offline-to-Online Multiobjective Optimization Strategy for Ship Integrated Energy System Economical/ Environmental Scheduling Problem

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.

Solutions and characterizations under multicriteria management systems

<p style='text-indent:20px;'>In real situations, agents might take different activity levels to participate; agents might represent administrative areas of different scales. On the other hand, agents always face an increasing need to focus on multiple aims efficiently in their operational processes. Thus, we introduce two solutions to investigate distribution mechanism by applying the maximal level-marginal contributions among activity level (decision) vectors under multicriteria management systems. Based on a specific reduced game and some reasonable properties, we offer some characterizations to analyze the rationality for these two solutions. In order to desire that any utility could be distributed among the players and their activity levels in proportion to related differences, two weighted extensions are also proposed by means of different weight functions.</p>

Efficient Access Control Permission Decision Engine Based on Machine Learning

Access control technology is critical to the safe and reliable operation of information systems. However, owing to the massive policy scale and number of access control entities in open distributed information systems, such as big data, the Internet of Things, and cloud computing, existing access control permission decision methods suffer from a performance bottleneck. Consequently, the large access control time overhead affects the normal operation of business services. To overcome the above-mentioned problem, this paper proposes an efficient permission decision engine scheme based on machine learning (EPDE-ML). The proposed scheme converts the attribute-based access control request into a permission decision vector, and the access control permission decision problem is transformed into a binary classification problem that allows or denies access. The random forest algorithm is used to construct a vector decision classifier in order to establish an efficient permission decision engine. Experimental results show that the proposed method can achieve a permission decision accuracy of around 92.6% on a test dataset, and its permission decision efficiency is significantly higher than that of the benchmark method. In addition, its performance improvement becomes more obvious as the scale of policy increases.

Hybrid-EPC: an Emperor Penguins Colony algorithm with crossover and mutation operators and its application in community detection

The idea of hybrid algorithms is formed due to the functional and structural differences in optimization algorithms. The goal is to create hybrid algorithms that can combine the strengths of the optimization algorithms to perform better in solving different problems. The Emperor Penguins Colony (EPC) algorithm is a population-based and nature-inspired optimization algorithm. This algorithm is powerful in finding global optima. In this paper, the standard EPC is improved by combining with genetic operators to finding better global optima. The genetic crossover and mutation operators have been used for modifying the decision vectors. These operators can cause a balance between exploration and exploitation. The balance between exploration and exploitation is effective in achieving a better optimal solution. The proposed algorithm called Hybrid-EPC is compared with GA, PSO, standard EPC, and Hybrid-PSO and tested on 20 various benchmark test functions. Also as an application, the proposed Hybrid-EPC algorithm is used for community detection in complex networks. For this purpose, the algorithm is tested on four social datasets and is compared with other community detection algorithms. The results show that this hybridization improves the standard EPC algorithm and has been successful in community detection.

A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution

To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hybrid evolutionary algorithm called GOA-DE for solving visual tracking problems. In the proposed hybrid algorithm, Grasshopper Optimization Algorithm (GOA) operates in refining the vector. In contrast, the Differential Evolution (DE) algorithm is used for transforming the decision vectors based on genetic operators. The improvement in maintaining the balance between exploration and exploitation abilities is made by incorporating genetic operators, namely, mutation and crossover in GOA. The success of GOA-DE is estimated by 23 classical benchmark functions, CEC05 functions, and CEC 2014 functions. The GOA-DE algorithm results prove that it is very viable associated with the metaheuristic up-to-date procedures. Similarly, visual tracking problems are resolved by the GOA-DE algorithm as a real challenging case study. Visual tracking several objects robustly in a video stream with complex backgrounds and objects are beneficial in subsequent generation computer vision structures. But, in exercise, it is problematic to plan an effective video-based visual tracking scheme owing to the fast-moving objects, probable occlusions, and diverse light circumstances. Investigational outcomes indicate that the GOA-DE-based tracker can energetically track a random target in many thought-provoking cases.

A time diversity scheme with multi-symbol detector for data transmission through flat fading Rayleigh channels

In this work, a new time diversity scheme is proposed to achieve time diversity in flat fading channels without affecting the data transmission rate. The conventional time diversity system reduces the transmission rate of the modulated symbols by a factor equal to the time diversity order. However, in the proposed time diversity system, any required time diversity order is achieved without reducing the transmission rate of the modulated symbols. In the proposed system, a diversity encoder maps vectors of N modulated symbols to vectors of N diversity symbols. By transmitting the diversity symbols, each modulated symbol is transmitted N times through N successive periods. Therefore, a time diversity with order N is achieved without affecting the modulated symbols rate since the transmission rate is N symbols per N channel uses. In the receiver, the outputs of a matched filter during the diversity period are stored in a buffer of length N. After filling the buffer, its contents are multiplied with the conjugate of the fading matrix. Multi-symbol detector removes interferences among the modulated symbols in the decision vector. It applies the algorithm of the diversity decoder and combines the corresponding received symbols to estimate the modulated symbols. The noise samples in the decision vector of the multi-symbol detector are uncorrelated. Maximum likelihood and linear decorrelator detectors are used as multi-symbol detectors. The performance of the proposed system is the same as the performance of the N channels diversity system with maximal ratio combiner receiver.

Distributed tracking-ADMM approach for chance-constrained energy management with stochastic wind power in smart grid

This paper proposes a distributed strategy to the chance-constrained energy management for smart grid with penetration of stochastic wind power. The energy management model is constructed including chance constraints of spinning reserves for the sake of guaranteeing the maximum utilization of wind power on the basis of reliability. With the available wind power characterizing by Weibull distribution, the chance constraints can be converted into deterministic ones by the derived analytical form of inverse cumulative distribution function. Although the original problem is transformed into a typical convex optimization problem, the tight coupling of constraints presents challenges to the design of distributed strategy. As such, we formulate the problem into a compact form with each generator unit depending on individual decision variables, instead of the common form with decision vector being the collection of all local decision variables. Then, by developing a new initialization method and an adaptive weight matrix selection method, a distributed strategy based on tracking Alternating Direction Method of Multipliers (ADMM) is proposed to solve the model. The simulation results indicate that the proposed distributed strategy achieves comparable performance to the corresponding centralized scenario, and better performance than distributed consensus-based ADMM in the related literature. Moreover, the validity of the proposed distributed strategy is confirmed in one day chance-constrained energy management with stochastic wind power.

Classification of Retroviruses Based on Genomic Data Using RVGC

Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnology Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage. Based on some significant results, the number of features used for classification is reduced to five. The classification shall be carried out in two phases. The first phase of classification shall select only two features. Unclassified data in the first phase is transferred to the next phase, where the final decision is taken with the remaining three features. Three data sets of animals and human retroviruses are selected; the training data set is used to minimize the classifier’s number and training; the validation data set is used to validate the models. The performance of the classifier is analyzed using the test data set. Also, we use decision tree, naive Bayes, k-nearest neighbors, and vector support machines to compare results. The results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.

Optimal Site and Size of Distributed Generator in Distribution Network Considering Active Power Loss Minimization

Distributed Generation (DG) allocation in distribution network is an optimal choice in maximizing benefits and reducing power losses. In this paper, self-adaptive differential evolution (SaDE), an optimization approach, is used for optimal site and capacity of DG. Different types of DGs such as solar PV and wind turbine (WT) at constant and near unity power factor are integrated into the distribution system. For validation of the proposed algorithm, IEEE 33-bus, 69-bus and 119-bus radial distribution networks are considered. The results show that the proposed algorithm has the ability to find global minimum value of objective function along with the appropriate site and capacity of solar PV and WT type DG. Moreover, the results of proposed method are compared with other existing techniques in order to show its effectiveness. The comparison shows that the proposed technique has the ability to get the lowest power losses with the smallest DG size. Thus, the proposed technique has the ability to find an optimal decision vector that makes it suitable for real-time applications.

Optimal Site and Size of Distributed Generator in Distribution Network Considering Active Power Loss Minimization

Distributed Generation (DG) allocation in distribution network is an optimal choice in maximizing benefits and reducing power losses. In this paper, self-adaptive differential evolution (SaDE), an optimization approach, is used for optimal site and capacity of DG. Different types of DGs such as solar PV and wind turbine (WT) at constant and near unity power factor are integrated into the distribution system. For validation of the proposed algorithm, IEEE 33-bus, 69-bus and 119-bus radial distribution networks are considered. The results show that the proposed algorithm has the ability to find global minimum value of objective function along with the appropriate site and capacity of solar PV and WT type DG. Moreover, the results of proposed method are compared with other existing techniques in order to show its effectiveness. The comparison shows that the proposed technique has the ability to get the lowest power losses with the smallest DG size. Thus, the proposed technique has the ability to find an optimal decision vector that makes it suitable for real-time applications.

Global optimization based on active preference learning with radial basis functions

This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

RETRACTED: Simulation of dance performance analysis with microprocessor and machine vision interaction

Dance is the expression of artists' favourite art forms such as express emotions, their body language, and the combination of dance art and stage effects in dance performance. In almost all genres of the art form, the effectiveness of the dance performance is largely due to the quality of the individual dancer's and group dance performance. Performing arts, particularly dance, it is one of the most important of intangible cultural heritage. However, due to the preservation, documentation, analysis, and visualization understanding of dance mode, it is difficult because of technical difficulties relations. The Proposed Machine Learning Support Decision Vector Machine (SDVM) algorithm and Field Programmable Gate Array (FPGA) is a dance expert watching dance due to the recognition task, the task knowledge of professional forecasters, gestures, and facial expressions and face-to-face conditions led to better synchronization of timing. In the proposed Machine learning SDVM algorithm, the results show that positive and dancers in the audience increased negative emotions; acceleration rate and body movement also increased. SDVM is classified as dancer performance based on the artist's facial expressions, stage performance, emotions. The simulation results show good results compared to other methods.

Bregman-style Online Convex Optimization with EnergyHarvesting Constraints

This paper considers online convex optimization (OCO) problems where decisions are constrained by available energy resources. A key scenario is optimal power control for an energy harvesting device with a finite capacity battery. The goal is to minimize a time-average loss function while keeping the used energy less than what is available. In this setup, the distribution of the randomly arriving harvestable energy (which is assumed to be i.i.d.) is unknown, the current loss function is unknown, and the controller is only informed by the history of past observations. A prior algorithm is known to achieve $O(\sqrtT )$ regret by using a battery with an $O(\sqrtT )$ capacity. This paper develops a new algorithm that maintains this asymptotic trade-off with the number of time steps T while improving dependency on the dimension of the decision vector from $O(\sqrtn )$ to $O(\sqrtłog(n) )$. The proposed algorithm introduces a separation of the decision vector into amplitude and direction components. It uses two distinct types of Bregman divergence, together with energy queue information, to make decisions for each component.

Distribution of Earthquakes on a Branching Fault System Using Integer Programming and Greedy‐Sequential Methods

AbstractA new global optimization method is used to determine the distribution of earthquakes on a complex, connected fault system. The method, integer programming, has been advanced in the field of operations research but has not been widely applied to geophysical problems until recently. In this application, we determine the optimal distribution of earthquakes on mapped faults to minimize the global misfit in slip rates for multifault ruptures. Integer programming solves for a decision vector composed of every possible location that a sample of earthquakes can occur on every fault, subject to slip rate uncertainty constraints. Step over connections are straightforward to include, whereas branching fault connections are not. To include branching ruptures, we distinguish between individual multifault rupture paths, as opposed to formulating the integer programming problem based on individual faults as in previous studies. The new method is applied to the complex fault system in the San Francisco Bay Area as a case study. Results from the integer programming method are compared to those from a local optimization method, termed the greedy‐sequential method. Several experiments using these two methods indicate that shape of the on‐fault magnitude distributions and which branching faults are involved in multifault ruptures depend on how much emphasis is placed on fitting the target slip rate. In cases where the underlying data are not strong enough to warrant chasing the target slip rate, it is better to focus on the distribution of feasible results that better represents the uncertainty in the solutions imposed by the data.

Time-varying multi-objective optimisation over switching graphs via fixed-time consensus algorithms

This paper considers distributed multi-objective optimisation problems with time-varying cost functions for network-connected multi-agent systems over switching graphs. The scalarisation approach is used to convert the problem into a weighted-sum objective. Fixed-time consensus algorithms are developed for each agent to estimate the global variables and drive all local copies of the decision vector to a consensus. The algorithm with fixed gains is first proposed, where some global information is required to choose the gains. Then, an adaptive algorithm is presented to eliminate the use of global information. The convergence of those algorithms to the Pareto solutions is established via Lyapunov theory for connected graphs. In the case of disconnected graphs, the convergence to the subsets of the Pareto fronts is studied. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.