Dimension Of Search Space Research Articles

Habitat dimensionality and feeding strategies but not temperature as determinants of body size-trophic structure relationship in a marine food web.

Disentangling the determinants of trophic structure is central to ecology. The capacity to capture subjugate and consume a prey (i.e. gape limitation) is a relevant limitation to acquire energy for most organisms, especially those in smaller size ranges. This generates a size hierarchy of trophic positions in which large organisms consume small ones. Body size is tightly correlated to gape limitation and explains a large fraction of variance in the body size-trophic position relationship. However, a considerable fraction of variance still remains to be explained. Consumer search space dimensionality (2D or 3D) and feeding strategies, temperature and the size structure of primary producers can alter the trophic structure, but tests based on information from natural food webs are scarce. We generated specific predictions about the body size trophic position relationship and evaluated them using information from a subtropical South Atlantic coastal marine ecosystem: benthic realm (2D, rocky shore and sandy beach) and the pelagic realm (3D). We characterized this marine coastal food web based on stable isotopes of carbon and nitrogen from 256 samples from primary producers (macroalgae and phytoplankton) to large predators (sand shark) in summer and winter. Consumer body size encompassed six orders of magnitude in weight from 10-2 to 6 × 104 g. Isotopic signal corresponded to an integration of carbon sources from basal consumers to top predators. The body size-trophic position relationship showed a linear positive association with different slopes for the benthic and pelagic environments. This implies a smaller predator prey size ratio for pelagic (3D) with respect to benthic consumers (2D) as theoretically expected. No seasonal differences were found in slopes and most of the overall variance in benthic environments was largely explained by feeding strategies of the different taxonomic groups. We provide an integrated evaluation on the role of body size, consumer search space and feeding strategy to understand the determinants of trophic position. Results demonstrate that integrating gape limitation hypothesis, the dimensionality of consumer search space and feeding strategies into a formal robust framework to understand trophic structure is feasible even in complex natural ecosystems.

Task scheduling in cloud computing based on grey wolf optimization with a new encoding mechanism

Task scheduling in the cloud computing still remains challenging in terms of performance. Several evolutionary-derived algorithms have been proposed to solve or alleviate this problem. However, evolutionary algorithms have good exploration ability, but the performance drops significantly in high dimensions. To address this issue, considering the characteristic of task scheduling in cloud computing (i.e. all task-VM mappings are 1-dimensional and have the same search range), we propose a task scheduling algorithm based on grey wolf optimization using a new encoding mechanism (GWOEM) in this work. Through this new encoding mechanism, greedy and evolutionary algorithms are rationally integrated in GWOEM. Besides, based on the new mechanism, the dimension of search space is reduced to 1 and the key parameter (i.e., the population size) is eliminated. We apply the proposed GWOEM to the Google Cloud Jobs dataset (GoCJ) and demonstrate better performance than the prior state of the art in terms of makespan.

Parallel hyperparameter optimization of spiking neural networks

Hyperparameter optimization of spiking neural networks (SNNs) is a difficult task which has not yet been deeply investigated in the literature. In this work, we designed a scalable constrained Bayesian based optimization algorithm that prevents sampling in non-spiking areas of an efficient high dimensional search space. These search spaces contain infeasible solutions that output no or only a few spikes during the training or testing phases, we call such a mode a “silent network”. Finding them is difficult, as many hyperparameters are highly correlated to the architecture and to the dataset. We leverage silent networks by designing a spike-based early stopping criterion to accelerate the optimization process of SNNs trained by spike timing dependent plasticity and surrogate gradient. We parallelized the optimization algorithm asynchronously, and ran large-scale experiments on heterogeneous multi-GPU Petascale architecture. Results show that by considering silent networks, we can design more flexible high-dimensional search spaces while maintaining a good efficacy. The optimization algorithm was able to focus on networks with high performances by preventing costly and worthless computation of silent networks.

Core allocation to minimize total flow time in a multicore system in the presence of a processing time constraint

Data centers are a vital and fundamental infrastructure component of the cloud. The requirement to execute a large number of demanding jobs places a premium on processing capacity. Parallelizing jobs to run on multiple cores reduces execution time. However, there is a decreasing marginal benefit to using more cores, with the speedup function quantifying the achievable gains. A critical performance metric is flow time. Previous results in the literature derived closed-form expressions for the optimal allocation of cores to minimize total flow time for a power-law speedup function if all jobs are present at time 0. However, this work did not place a constraint on the makespan. For many diverse applications, fast response times are essential, and latency targets are specified to avoid adverse impacts on user experience. This paper is the first to determine the optimal core allocations for a multicore system to minimize total flow time in the presence of a completion deadline (where all jobs have the same deadline). The allocation problem is formulated as a nonlinear optimization program that is solved using the Lagrange multiplier technique. Closed-form expressions are derived for the optimal core allocations, total flow time, and makespan, which can be fitted to a specified deadline by adjusting the value of a single Lagrange multiplier. Compared to the unconstrained problem, the shortest job first property for optimal allocation is maintained; however, a number of other properties require revising and other properties are only retained in a modified form (such as the scale-free and size-dependence properties). It is found that with a completion deadline the optimal solution may contain groups of simultaneous completions. In general, all possible patterns of single- and group-completion need to be considered, producing an exponential search space. However, the paper determines analytically that the optimal completion pattern consists of a sequence of single completions followed by a single group of simultaneous completions at the end, which reduces the search space dimension to being linear. The paper validates the Lagrange multiplier approach by verifying constraint qualifications.

Cube-Evo: A Query-Efficient Black-Box Attack on Video Classification System

The current progressive research in the domain of black-box adversarial attack enhances the reliability of deep neural network (DNN)-based video systems. Recent works mainly carry out black-box adversarial attacks on video systems by query-based parameter dimension reduction. However, the additional temporal dimension of video data leads to massive query consumption and low attack success rate. In this article, we embark on our efforts to design an effective adversarial attack on popular video classification systems. We deeply root the observations that the DNN-based systems are sensitive to adversarial perturbations with high frequency and reconstructed shape. Specifically, we propose a systematic attack pipeline Cube-Evo, aiming to reduce the search space dimension and obtain the effective adversarial perturbation via the optimal parameter group updating. We evaluate the proposed attack pipeline on two popular datasets: UCF101 and JESTER. Our attack pipeline reduces query consumption and achieves a high success rate on various DNN-based video classification systems. Compared with the state-of-the-art method Geo-Trap-Att, our pipeline averagely reduces 1.6× query consumption in untargeted attacks and 2.9× in targeted attacks. Besides, Cube-Evo improves 13% attack success rate on average, achieving new state-of-the-art results over diverse video classification systems.

Enhancement of Reflood Test Prediction by Integrating Machine Learning and Data Assimilation Technique

Data assimilation (DA) was revealed as a highly efficient approach to enhance the prediction’s accuracy, demonstrating the reliability of the simulation through uncertainty quantification analysis. However, in the numerical simulations, most of the tasks are highly nonlinear relationships between the parameters that directly affect the efficiency of the DA such as the large search space dimension, resulting in significant computational costs. This limitation can be overcome by implementing machine learning (ML) predictions that can efficiently expand the DA search space. Therefore, this study is aimed at enhancing the performance of DA by integrating ML and DA (IMD), proposing a new method to overcome the stand-alone DA limitations. The approach involved implementing a stand-alone DA using the multidimensional analysis of reactor safety (MARS) code to enhance the prediction of reflood tests. The output datasets obtained from the stand-alone DA were then used to train the deep neural networks (DNNs), and the accuracy of the DNN’s prediction was thoroughly evaluated with varying dataset sizes. This DNN subsequently can predict the enormous unobserved samples, enabling the identification and investigation of the prospective candidates for the subsequent DA process. The results demonstrated that the stand-alone DA achieved an accuracy enhancement of up to 41.3% in reflood test predictions, while the IMD yielded even more significant improvements, with a performance enhancement of 47.0%. These findings reveal that the IMD approach outperformed the stand-alone DA approach, particularly in the case of high flooding rate tests. In this context, the proposed integrating system can effectively overcome the high computational cost and enhance the performance of stand-alone DA.

On polling directions for randomized direct-search approaches: application to beam angle optimization in intensity-modulated proton therapy

AbstractDeterministic direct-search methods have been successfully used to address real-world challenging optimization problems, including the beam angle optimization (BAO) problem in radiation therapy treatment planning. BAO is a highly non-convex optimization problem typically treated as the optimization of an expensive multi-modal black-box function which results in a computationally time consuming procedure. For the recently available modalities of radiation therapy with protons (instead of photons) further efficiency in terms of computational time is required despite the success of the different strategies developed to accelerate BAO approaches. Introducing randomization into otherwise deterministic direct-search approaches has been shown to lead to excellent computational performance, particularly when considering a reduced number (as low as two) of random poll directions at each iteration. In this study several randomized direct-search strategies are tested considering different sets of polling directions. Results obtained using a prostate and a head-and-neck cancer cases confirmed the high-quality results obtained by deterministic direct-search methods. Randomized strategies using a reduced number of polling directions showed difficulties for the higher dimensional search space (head-and-neck) and, despite the excellent mean results for the prostate cancer case, outliers were observed, a result that is often ignored in the literature. While, for general global optimization problems, mean results (or obtaining the global optimum once) might be enough for assessing the performance of the randomized method, in real-world problems one should not disregard the worst-case scenario and beware of the possibility of poor results since, many times, it is only possible to run the optimization problem once. This is even more important in healthcare applications where the mean patient does not exist and the best treatment possible must be assured for every patient.

Surrogate methods for determining profiles of material properties of planar test objects with accumulation of apriori information about them

New methods for identifying the material properties of planar objects as a result of measurements by the eddy current method are proposed. The methods are based on the latest surrogate strategies and advanced optimization techniques that improve efficiency and reduce resource consumption of problem solutions, and balance computational complexity with the accuracy of the results. High-performance metamodels for global surrogate optimization are based on deep truly meaningful fully connected neural networks, serving as an additional function of accumulating apriori information about objects. High accuracy of the approximation of the multidimensional response surface, which is determined by the “exact” electrodynamic model of the testing process, is ensured by performing calculations according to the computer design of a homogeneous experiment with a low weighted symmetric centered discrepancy. The results of numerical experiments performed for full and reduced dimensional search spaces, which can be obtained by linear transformations using the principal component method, are presented. The verification of the methods proved their sufficiently high accuracy and computational performance.

Characterization of rankings generated by pseudo-Boolean functions

In this paper we pursue the study of pseudo-Boolean functions as ranking generators. The objective of the work is to find new insights between the relation of the degree m of a pseudo-Boolean function and the rankings that can be generated by these insights. Based on a characterization theorem for pseudo-Boolean functions of degree m, several observations are made. First, we verify that pseudo-Boolean functions of degree m<n, where n is the search space dimension, cannot generate all the possible rankings of the solutions. Secondly, the sufficient condition for a ranking to be generated by a pseudo-Boolean function of dimension (n−1) is presented, and also the necessary condition is conjectured. Finally, we observe that the same argument is not sufficient to prove which ranking can be generated by pseudo-Boolean functions of degree m<n−1.

A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization

Sparse large-scale multi-objective optimization problems (LSMOPs), which are characterized by high dimensional search space and sparse Pareto optimal solutions, have a widespread existence in academic research and practical applications. While the high dimensional decision space poses challenges to multi-objective evolutionary algorithms (MOEAs), the difficulty of solving sparse LSMOPs can be alleviated by utilizing the prior knowledge that the optimal solutions are sparse. In this paper, a co-evolutionary algorithm based on sparsity clustering, namely SCEA, is proposed, where the prior knowledge of sparse optimal solutions is utilized explicitly. At each generation, SCEA first calculates the current optimal sparsity by sparsity clustering. Then, SCEA divides the population into a winner subpopulation and two loser subpopulations. While the winner subpopulation reproduces offspring solutions by conventional genetic operators, the loser subpopulations generate offspring solutions along two competitive directions under the guidance of current optimal sparsity and variable importance. In the experiments, four state-of-the-art MOEAs are selected as the comparative algorithms. Experimental results show that the proposed algorithm is superior to the four competitors on both benchmark problems and practical applications, which include the sparse signal reconstruction problem, the community detection problem, and the instance selection problem.

Solving large-scale combined heat and power economic dispatch problems by using deep reinforcement learning based crisscross optimization algorithm

The extensive interconnection in bulk power systems brings great challenges in the economic dispatch problems, especially the large-scale combined heat and power economic dispatch (CHPED), which is quite intractable due to complex thermal and electrical couplings in cogeneration units. Most meta-heuristic algorithms cannot work well while considering non-convexity, discontinuity, and non-differentiability. Particularly, when it comes to the high dimensional and large-scale CHPED problems, these algorithms are either easy to trap in the local optimum or quite time-consuming. To address these issues, a novel deep reinforcement learning (DRL) based crisscross optimization (CSO) algorithm is proposed for the first time, which can (1) improve the searching ability of CSO, (2) enhance the overall searching efficiency, and (3) perform extraordinarily well for the large-scale CHPED problems. Firstly, to further improve the global search ability, DRL is employed to reduce dimensionality and narrow the search space of the initial population of the CSO algorithm. Then, DRL is updated by the deep deterministic policy gradient (DDPG) strategy, which can facilitate an instantaneous decision in accelerating CSO. Thereafter, by adopting each decision provided by DRL, a great difference may occur in the same scenario to speed up the crossover procedure and guide the upcoming search round. On this basis, the enhanced CSO algorithm could quickly carry out crossovers with search space contraction and dimensionality reduction. Finally, experiments on four test systems have substantiated the effectiveness of the proposed algorithm, especially the superiority over other state-of-the-art techniques in solving large-scale CHPED problems.

Low-order gray-box modeling of heating buildings and the progressive dimension reduction identification of uncertain model parameters

Heating buildings have a huge heat capacity and have abilities to accept fluctuating heat supply from renewables. Creating a low-order scalable model that describes the thermal dynamics of coupled heat transfer, charge, and discharge processes among heating buildings is essential to unlocking the thermal storage potential of building mass and achieving space heating decarbonization. White-box models tend to be too complex and black-box models lack scalability. Thus, a low-order gray-box model with uncertain lumped parameters is proposed based on comprehensive heat transfer analyses and necessary assumptions. Thermal mass in heating buildings is divided into four independent heat storage units. The building envelope that can be heated directly or not is regarded as a different heat storage unit. To reduce the search space dimension of model identification, the decision variables are downsized to essential model parameters based on the sensitivity analysis. Moreover, a progressive dimension reduction method is introduced to further improve the probability that the optimization algorithm converges to a global minimum. Case studies show that the calibrated model with global optimal solutions of uncertain model parameters has a good performance on the prediction of the average temperature of indoor air, the average absolute error is only 0.1 °C.

Maximizing Performance and Stability of Organic Solar Cells at Low Driving Force for Charge Separation.

Thanks to the development of novel electron acceptor materials, the power conversion efficiencies (PCE) of organic photovoltaic (OPV) devices are now approaching 20%. Further improvement of PCE is complicated by the need for a driving force to split strongly bound excitons into free charges, causing voltage losses. This review discusses recent approaches to finding efficient OPV systems with minimal driving force, combining near unity quantum efficiency (maximum short circuit currents) with optimal energy efficiency (maximum open circuit voltages). The authors discuss apparently contradicting results on the amount of exciton binding in recent literature, and approaches to harmonize the findings. A comprehensive view is then presented on motifs providing a driving force for charge separation, namely hybridization at the donor:acceptor interface and polarization effects in the bulk, of which quadrupole moments (electrostatics) play a leading role. Apart from controlling the energies of the involved states, these motifs also control the dynamics of recombination processes, which are essential to avoid voltage and fill factor losses. Importantly, all motifs are shown to depend on both molecular structure and process conditions. The resulting high dimensional search space advocates for high throughput (HT) workflows. The final part of the review presents recent HT studies finding consolidated structure-property relationships in OPV films and devices from various deposition methods, from research to industrial upscaling.

An archive-based self-adaptive artificial electric field algorithm with orthogonal initialization for real-parameter optimization problems

In this article, a series of learning strategies are proposed to enhance the optimization ability of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool that can greatly influence the adaptability of population-based optimization algorithms. This article proposes, (i) an orthogonal array-based learning strategy to generate a better initial population for the artificial electric field algorithm. Along with the changes in the initialization mechanism, this article also proposes, (ii) an archive-based self-adaptive learning strategy for an artificial electric field algorithm. The proposed learning strategy divides the population into ordinary and extraordinary sub-populations, each with distinct learning mechanisms. The ordinary sub-population utilizes six learning strategies based on three archives, which contain individuals of different quality levels. We incorporate, (iii) a mutation strategy also to update the extraordinary sub-population. Finally, (iv) a self-adaptive strategy is implemented to dynamically adjust the parameters of the proposed algorithm. The effectiveness of these mechanisms is assessed through an extensive analysis of exploration–exploitation dynamics and diversity. Furthermore, an independent structural study is conducted to examine the impact of implemented mechanisms on the algorithm’s behavior and efficiency. The proposed algorithm is evaluated on real parameter CEC 2017 problems across different dimensional search spaces. It is compared to eleven state-of-the-art algorithms, and the results demonstrate superior performance in terms of solution accuracy, convergence rate, search capability, and stability. The overall ranking highlights its exceptional potential for solving challenging optimization problems. Additionally, it outperforms other state-of-the-art algorithms across various dimensions, achieving accuracy rates of 64.48%, 70.05%, 78.73%, and 79.25% for dimensions 10, 30, 50, and 100, respectively. Furthermore, it demonstrates superior performance, outperforming others in 73.13% and 60.61% of the problems concerning average accuracy and statistical significance across all dimensions, respectively.

On a Population Sizing Model for Evolution Strategies Optimizing the Highly Multimodal Rastrigin Function.

A model is presented that allows for the calculation of the success probability by which a vanilla Evolution Strategy converges to the global optimizer of the Rastrigin test function. As a result a population size scaling formula will be derived that allows for an estimation of the population size needed to ensure a high convergence security depending on the search space dimensionality.

Fast Force-Closure Grasp Synthesis With Learning-Based Sampling

Anthropomorphic robotic hands have been widely investigated to dexterously manipulate objects because of their anatomical similarity to the human hand. However, the large dimension of configuration space challenges the real-time performance of existing grasp planning methods and drastically limits the application of anthropomorphic hands. In this letter, we propose a fast force-closure grasp synthesis (FFCGS) method for the anthropomorphic hand to efficiently grasp unknown objects. The FFCGS is implemented by using a signed distance field (SDF) as input. Firstly, a network that samples feasible 6D wrist poses is trained in an end-to-end fashion to reduce the dimension of search space. Furthermore, a fast optimization algorithm is presented to find finger configurations for force-closure precision grasp based on the differentiable Q-distance metric. We validate our method in both a simulated and a real-world environment. Experiment results show that the proposed FFCGS achieves a significantly improved performance in terms of time efficiency (5 times faster), grasp quality metrics, and success rate (5%-10% improvement) over benchmark methods. The outcomes of this study have great significance in promoting the motion planning of robot hand-arm systems and upper-limb prostheses.

A foreground digital calibration algorithm for time-interleaved ADCs with low computational complexity

In front of the offset, gain, timing, and bandwidth mismatch errors, time-interleaved analog-to-digital converters (TIADCs) are usually calibrated to achieve satisfying performance. In this paper, we propose a new digital calibration approach for TIADCs, including the direction-distance search algorithm and multiplier-free gradient descent method. Compared to state-of-the-art multiplication-less techniques, this approach significantly reduces time complexity by minimizing search space dimension, subtracting the number of searches, and varying iteration step size. Notably, no multiplier is needed in the mismatch estimation, resulting in a much lower computational complexity than in previous work. For a 12-bit 4-channel TIADC, simulation results show that the signal-to-noise-and-distortion ratio is improved from 25 dB to 65 dB within the duration of 1600 samples. The proposed calibration circuit is synthesized targeting standard 28-nm technology, occupying the area of 0.014 mm2 and dissipating the power of 19 mW at 2.8 GHz clock frequency.

Parallel particle swarm optimization for optimal SAGD operating scenario and well placement in a high dimensional search space

Steam-assisted gravity drainage (SAGD) has been commercially tested successfully in many oilfields with different characteristics. The operational parameters and wells’ location substantially affect the performance of SAGD, and designing a scenario optimization to find the optimum values of these parameters would enhance the efficiency of the process. Although most previous studies worked on optimizing some of the mentioned parameters separately, barely any optimized them simultaneously using a full 3D model since optimizing such a high-dimensional problem is time-consuming. In this study, whole-time scenario optimization is designed to simultaneously optimize the operational (steam injection rate, temperature, and production bottom hole pressure of all intervals) and well placement parameters of an 8-year SAGD. Parallel particle swarm optimization (PSO) is developed to determine the desired number of simulations concurrently to reduce computational costs. Also, the SAGD time is divided into four time intervals, and the effect of uniform and non-uniform time intervals on the net present value (NPV) and oil production is investigated. The outcomes showed that conducting parallel PSO would reduce the optimization time by 79.8%. Also, by applying the non-uniform intervals, the highest NPV is achieved, and the SAGD time declines to 6 years.

A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed.

Multiobjective planning of indoor Wireless Local Area Networks using subpermutation-based hybrid algorithms

Wireless Local Area Network (WLAN) has become the most popular technology for mobile Internet access in recent decades. This manuscript presents a novel approach, based on hybrid optimization algorithms, for planning WLANs. Two objective functions are optimized: to maximize network load balance and signal-to-noise ratio. In addition, constraints related to coverage, customer, and equipment demand are considered. A key aspect of the proposed algorithm is its new representation/decoding scheme, based on subpermutations, which considerably reduces the search space dimension. This structure guarantees feasibility of the obtained solutions and increases the computational efficiency of the method. Several tests were performed in two scenarios, one of them using real data from a large-scale WLAN. When compared to other three approaches, such results show that the proposed method provides solutions that reduce costs and improve the WLAN throughput.