Global Optimization Objective Research Articles

On solving close enough orienteering problems with overlapped neighborhoods

The Close Enough Traveling Salesman Problem (CETSP) is a well-known variant of the classic Traveling Salesman Problem whereby the agent may complete its mission at any point within a target neighborhood. Heuristics based on overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention in addressing CETSPs. While SZs offer effective approximations to the original graph, their inherent overlap imposes constraints on the search space, potentially conflicting with global optimization objectives. Here we show how such limitations can be converted into advantages in the Close Enough Orienteering Problem (CEOP) by aggregating prizes across overlapped neighborhoods. We further extend the classic CEOP with Non-uniform Neighborhoods (CEOP-N) by introducing non-uniform cost considerations for prize collection. To tackle CEOP (and CEOP-N), we develop a new approach featuring a Randomized Steiner Zone Discretization (RSZD) scheme coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and Ant Colony System (ACS) — CRaSZe-AntS. The RSZD scheme identifies sub-regions for PSO exploration, and ACS determines the discrete visiting sequence. We evaluate the RSZD’s discretization performance on CEOP instances derived from established CETSP instances and compare CRaSZe-AntS against the most relevant state-of-the-art heuristic focused on single-neighborhood optimization for CEOP instances. We also compare the performance of the interior search within SZs and the boundary search on individual neighborhoods in the context of CEOP-N. Our experimental results show that CRaSZe-AntS can yield comparable solution quality with significantly reduced computation time compared to the single neighborhood strategy, where we observe an averaged 140.44% increase in prize collection and 55.18% reduction of algorithm execution time. CRaSZe-AntS is thus highly effective in solving emerging CEOP-N, examples of which include truck-and-drone delivery scenarios.

Gradient-based multi-label feature selection considering three-way variable interaction

Nowadays, Multi-Label Feature Selection (MLFS) attracts more and more attention to tackle the high-dimensional problem in multi-label data. A key characteristic of existing gradient-based MLFS methods is that they typically consider two-way variable correlations between features and labels, including feature-feature and label-label correlations. However, two-way correlations are not sufficient to steer feature selection since such correlations vary given different additional variables in practical scenarios, which leads to the selected features with relatively-poor classification performance. Motivated by this, we capture three-way variable interactions including feature-feature-label and feature-label-label interactions to further characterize the fluctuating correlations in the context of another variable, and propose a new gradient-based MLFS approach incorporating the above three-way variable interactions into a global optimization objective. Specifically, based on information theory, we develop second-order regularization penalty terms to regard three-way interactions while jointly combining with the main loss term in regard to feature relevance. Then the objective function can be efficiently optimized via a block-coordinate gradient descent schema. Meanwhile, we provide a theoretical analysis demonstrating the effectiveness of the regularization terms in exploiting three-way interaction. In addition, experiments conducted on a series of benchmark data sets also verify the validity of the proposed method on multiple evaluation metrics.

Event-triggered distributed optimization of multi-agent systems with time delay.

In this article, the distributed optimization based on multi-agent systems was studied, where the global optimization objective of the optimization problem is a convex combination of local objective functions. In order to avoid continuous communication among neighboring agents, an event-triggering algorithm was proposed. Time delay was also considered in the designed algorithm. The triggering time of each agent was determined by the state measurement error, the state of its neighbors at the latest triggering instant and the exponential decay threshold. Some sufficient conditions for optimal consistency were obtained. In addition, Zeno-behavior in triggering time sequence was eliminated. Finally, a numerical simulation was given to prove the effectiveness of the proposed algorithm.

Fixed‐time distributed optimization for multi‐agent systems with external disturbances over directed networks

AbstractThis article considers the fixed‐time distributed optimization problem of multi‐agent systems with external disturbances, in which the global optimization objective is a convex combination of local objective functions. To solve this issue, a directed communication network is carefully designed, and an integral sliding mode control protocol is proposed based on the gradient of global objective function first. Moreover, two distributed optimal protocols are designed by using the gradient and the Hessian matrix of local objective function, respectively. By employing Lyapunov stability theory, graph theory, convex analysis, and inequality techniques, we prove that all proposed protocols can make agents achieve consensus and converge accurately to the optimal solution of the considered problem in some fixed‐time intervals. Finally, some numerical simulations are given to verify the feasibility of the theoretical results.

Scalable Distributed Hashing for Approximate Nearest Neighbor Search.

Hashing has been widely applied to the large-scale approximate nearest neighbor search problem owing to its high efficiency and low storage requirement. Most investigations concentrate on learning hashing methods in a centralized setting. However, in existing big data systems, data is often stored across different nodes. In some situations, data is even collected in a distributed manner. A straightforward way to solve this problem is to aggregate all the data into the fusion center to obtain the search result (aggregating method). However, this strategy is not feasible because of the prohibitive communication cost. Although a few distributed hashing methods have been proposed to reduce this cost, they only focus on designing a distributed algorithm for a specific global optimization objective without considering scalability. Moreover, existing distributed hashing methods aim at finding a distributed solution to hashing, meanwhile avoiding accuracy loss, rather than improving accuracy. To address these challenges, we propose a Scalable Distributed Hashing (SDisH) model in which most existing hashing methods can be extended to process distributed data with no changes. Furthermore, to improve accuracy, we utilize the search radius as a global variable across different nodes to achieve a global optimum search result for every iteration. In addition, a voting algorithm is presented based on the results produced by multiple iterations to further reduce search errors. Theoretical analyses of communication, computation, and accuracy demonstrate the superiority of the proposed model. Numerical simulations on three large-scale and two relatively small benchmark datasets also show that the SDisH model achieves up to 44.75% and 10.23% accuracy gains compared to the aggregating method and state-of-the-art distributed hashing methods, respectively.

A novel quadruple generative adversarial network for semi-supervised categorization of low-resolution images

In order to make utilization of unlabeled low-resolution (LR) images to shape discriminative models, we present quadruple generative adversarial network (Q-GAN), a game-theoretical framework for implementing semi-supervised categorization of LR images. It can realize photo-realistic image super-resolution (SR) and semi-supervised pattern recognition simultaneously. We consider our pipeline as a four-player optimization-based formulation, which consists of four vital components, i.e., a refiner for image SR and generation, a discriminator for identifying high-resolution (HR) samples and another for identifying true (original) samples, a classifier for label prediction. The refiner and two discriminators characterize the conditional distributions between images and labels, whilst the classifier solely focuses on predicting real image-label pairs. We select those high-quality super-solved images with ground-truth labels for data supplement. We customize the global optimization objective function as well as the training procedure to ensure model approximates the posterior distribution of latent variables given true data in a semi-supervised manner. Experimental results demonstrate that Q-GAN can simultaneously (1) deliver the promising categorization performance among state-of-the-arts, i.e., validation accuracy achieves 92.18% and testing accuracy achieves 90.63%, and (2) recover fine-grained textures with high peak signal-to-noise ratios (PNSRs) and structural similarities (SSIMs) from heavily downsampled testing images of hand-crafted dataset and public benchmarks.

Hierarchical dispatch of multiple microgrids using nodal price: an approach from consensus and replicator dynamics

A hierarchical approach for the energy management of geographically close microgrids connected through a dedicated AC power network is proposed in this paper. The proposed approach consists of a two-layer energy management system (EMS) for networked microgrids. In the lower layer, each microgrid solves its own economic dispatch problem through a distributed model predictive control approach that respects capacity limits and ramp-rate constraints of distributed generation. In the upper layer, the energy trading in the network of microgrids decides how to optimally trade the energy based on the marginal cost information from the lower layer in order to improve global optimization objectives, e.g., social welfare. In order to solve the trading problem, a consensus-based algorithm and a replicator dynamics algorithm are proposed assuming that the marginal cost function of the microgrid is known and linear. It is shown that both algorithms converge to the same solution, which is equivalent to the minimization of operation costs. The consensus-based algorithm is extended in order to tackle more general marginal cost functions and trading network constraints. Moreover, the effect of ramp constraints and network limits is studied. Simulations show the effectiveness of the proposed algorithms for three interconnected microgrids with different characteristics.

Network optimization for unified packet and circuit switched networks

Internet traffic continues to grow relentlessly, driven largely by increasingly high resolution video content. Although studies have shown that the majority of packets processed by Internet routers are pass-through traffic, they nonetheless have to be queued and routed at every hop in current networks, which unnecessarily adds substantial delays and processing costs. Such pass-through traffic can be better circuit-switched through the underlying optical transport network by means of pre-established circuits, which is possible in a unified packet and circuit switched network. In this paper, we propose a novel convex optimization framework based on a new destination-based multicommodity flow formulation for the allocation of circuits in such unified networks. In particular, we consider two deployment settings, one based on real-time traffic monitoring, and the other relying upon history-based traffic predictions. In both cases, we formulate global network optimization objectives as concave functions that capture the fair sharing of network capacity among competing traffic flows. The convexity of our problem formulations ensures globally optimal solutions.

Layout optimization of landing gears for an underwater glider based on particle swarm algorithm

A novel optimization process is proposed to find the optimal layout of landing gears for an underwater glider, based on which the comprehensive performance of the underwater landing platform is significantly improved. The performance of the platform is defined by 4 sub optimization objectives. The weights of the sub objectives are determined according to the degree of importance. 2 kinds of instability states of the platform are defined. Numerical investigations are carried out to analyze the hydrodynamic performance of the platform using Ansys CFX. Then, response surface models of the optimization objectives are established using 30 sample points (design schemes). The response surface models describe the relationship between the optimization objectives and layout parameters of the landing gears. Particle swarm optimization (PSO) is applied to search for the optimal solution accompanied with the response surface models. Finally, the value of the global optimization objective is enhanced from −0.1601 to −0.1147.

Orthogonal design for scale invariant feature transform optimization

To improve object recognition capabilities in applications, we used orthogonal design (OD) to choose a group of optimal parameters in the parameter space of scale invariant feature transform (SIFT). In the case of global optimization (GOP) and local optimization (LOP) objectives, our aim is to show the operation of OD on the SIFT method. The GOP aims to increase the number of correctly detected true matches (NoCDTM) and the ratio of NoCDTM to all matches. In contrast, the LOP mainly aims to increase the performance of recall–precision. In detail, we first abstracted the SIFT method to a 9-way fixed-effect model with an interaction. Second, we designed a mixed orthogonal array, MA(64,23420,2), and its header table to optimize the SIFT parameters. Finally, two groups of parameters were obtained for GOP and LOP after orthogonal experiments and statistical analyses were implemented. Our experiments on four groups of data demonstrate that compared with the state-of-the-art methods, GOP can access more correct matches and is more effective against object recognition. In addition, LOP is favorable in terms of the recall–precision.

NO EVIDENCE FOR ACTIVITY CORRELATIONS IN THE RADIAL VELOCITIES OF KAPTEYN’S STAR

ABSTRACT Stellar activity may induce Doppler variability at the level of a few m s−1 which can then be confused by the Doppler signal of an exoplanet orbiting the star. To first order, linear correlations between radial velocity measurements and activity indices have been proposed to account for any such correlation. The likely presence of two super-Earths orbiting Kapteyn’s star was reported in Anglada-Escudé et al., but this claim was recently challenged by Robertson et al., who argued for evidence of a rotation period (143 days) at three times the orbital period of one of the proposed planets (Kapteyn’s b, P = 48.6 days) and the existence of strong linear correlations between its Doppler signal and activity data. By re-analyzing the data using global statistics and model comparison, we show that such a claim is incorrect given that (1) the choice of a rotation period at 143 days is unjustified, and (2) the presence of linear correlations is not supported by the data. We conclude that the radial velocity signals of Kapteyn’s star remain more simply explained by the presence of two super-Earth candidates orbiting it. We note that analysis of time series of activity indices must be executed with the same care as Doppler time series. We also advocate for the use of global optimization procedures and objective arguments, instead of claims based on residual analyses which are prone to biases and incorrect interpretations.

A Hierarchical Multilayer Service Composition Model for Global Virtual Organizations

A major benefit of service composition is the ability to support agile global collaborative virtual organizations. However, being global in nature, collaborative virtual organizations can have several virtual industry clusters (VIC), where each VIC has hundreds to thousands of virtual enterprises that provide functionally similar services exposed as web services. These web services can be differentiated on a high dimensionality of quality of service attributes. The dilemma the virtual enterprise broker is faced with is how to dynamically select the best combination of component services to fulfill a complex consumer need within the shortest time possible. This composite service selection problem remains a Multi-Criteria Decision Making (MCDM) NP hard problem. Although existing MCDM methods based on local planning are linearly scalable for large problems, they lack capabilities to express critical intertask constraints that are practically relevant to service consumers. MCDM global planning methods on the other hand suffer exponential state space explosion making them severely limited for large problems of industrial relevance. This paper proposes HMSCM: Hierarchical Multi-Layer Service Composition Model. HMSCM is based on the theory of Layering as Optimization Decomposition (28-31). We view the service selection process as a layer network where each layer is a subproblem to be solved. The objective of one of the layers is to maximize a local utility function over a subset of web service QoS attributes from a service consumer perspective. The objective of the other layer is to maximize a local utility function over another subset of web service QoS attributes from the perspective of the Virtual enterprise broker. We develop the algorithm: Service Layered Utility Maximization (SLUM) that extends the Mixed Integer programming model in (9). We then formulate the problem at each layer in form of SLUM. Together, the two layers attempt to achieve the global optimization objective of the network. We show analytically how HMSCM overcomes the shortcomings of existing local planning and global planning service selection methods while retaining the strengths from each. i.e HMSCM is able to scale linearly with increasing number of QoS variables and number of web services while being able to enforce global intertask constraints.

Fuzzy configuration of matching runtime implementation strategies

With applications currently growing in complexity and range, increasing numbers of configuration problems are arising in compilers. Already many software systems offer multiple specialized implementation strategies and substrategies, differing in terms of applicability and/or cost, depending on the application context. Configurations then have to be created from the different strategies available in accordance with the application characteristics, the global optimization objective, and potential constraints on the strategies' combinability. In many cases, this results in a combinatorial, i.e., discrete, optimization problem. Proper solutions for automating the configuration while limiting the complexity of the solution search are still being sought. We address here the field of parallel/distributed processing and the configuration of runtime implementation strategies, such as for communication or dynamic load balancing. We present a rule-based approach, integrating fuzzy methodologies for the classification of application characteristics and for gradual selection preference in rules. In this way we exploit available knowledge about the correlation of the problem and solution space, and apply soft computing methods to obtain an approximate, rather than perfect, solution approach, thus helping to limit the configuration complexity. Our approach extends standard fuzzy inference by a multistage organization, and – with proper organization of rules, characteristics and strategies – performs hierarchical fuzzy inference. The approach is demonstrated on concrete configuration examples in parallel compilers.