Multidimensional Search Space Research Articles

Bass Re-visited: Quantifying Multi-Finality

As forecasting the diffusion of new technologies results in unreliable predictions when only few trend data are available, scholars and practitioners alike tend to favor foresight efforts of a more qualitative nature to gauge foreseeable futures. We argue and demonstrate that it is feasible to reconcile quantitative forecasting models with (qualitative) scenario development efforts by advancing a set of heuristics that build on known diffusion curves. Rather than focusing on the ultimate set of parameter estimates, we explore an exhaustive range of combinations signaling potential outcomes. We demonstrate this approach for Battery Electric Vehicles in Europe and the US. A multi-dimensional search space is developed consisting of a wide range of values for each parameter implied. Resulting outcomes are assessed in terms of how well they explain current observations (2011-2017) by means of a loss function. The loss function allows us to evaluate the presence of different end states (multi-finality), their frequency of occurrence, and the implied time frames. The insights obtained inform foresight exercises in a number of distinctive ways. First, by incorporating multi-finality in the design of the heuristics, it becomes feasible to qualify multiple pathways ex ante and simultaneously. This allows us to assess the likelihood of - and the time horizon implied by - different scenarios. Second, allowing for multiple pathways and end states directs our attention to the antecedents required for different scenarios to unfold, which can inspire back-casting efforts of a more qualitative nature. Finally, applying the grid logic on more fine-grained levels of analysis (e.g. countries, states, or regions) helps us assess the (differential) impact of policies oriented towards stimulating diffusion. As such, quantitative heuristics become a valuable complement to scenario development exercises, rather than an inferior or even neglected substitute.

ОЦІНКА ТОЧНОСТІ НЕЙРОМЕРЕЖЕВИХ МЕТАМОДЕЛЕЙ КРУГОВИХ НАКЛАДНИХ ВИХРОСТРУМОВИХ ПЕРЕТВОРЮВАЧІВ

For the problem of synthesis of an eddy current probe with a homogeneous sensitivity zone, its RBF-metamodels which have high computational efficiency are created. These metamodels can be used to design a probe with a given distribution of the density of eddy currents at testing points of the space located on the surface of the conductive object in the testing area of the probe. The excitation coil of a concentric surface eddy current probe is represented by an actuator which is powered by an alternating current and located above the testing object with constant electrophysical parameters. The obtained metamodels are checked for adequacy and informativeness on a complex of statistical indicators with an objective estimation of their statistical significance. At the approximation of the response surface, the computer experiment plan was used, namely, the multidimensional search space was filled with points generated by means of LPτ-sequences that were evenly located on the response surface. The reproducibility of the review surface is checked using the resulting metamodel throughout the modeling area. A reasonable approximation error has been achieved. The results of numerical experiments show the effectiveness of the use of RBF-metamodel for the response surface approximation. The resulting metamodels have an average model error of 6.76 % for the first subregion, 4.8 % for the second and 4.78 % for the third one. Created metamodel is adequate according to Fisher criterion; informative on the coefficient of determination and significantly reliable. At the same time, it allows to reduce the workload of calculations in the tasks of computer designing of eddy current probes. This opens up new possibilities for the synthesis of surface eddy current probes – both parametric and structural-parametric ones.

FZ enabled Multi-objective PSO for multicasting in IoT based Wireless Sensor Networks

Real-time services for IoT enabled Disastrous Management Scenarios (DMSs), multicast routing problem has attracted many researchers due to its NP-hard behaviour. The objective is to find a Steiner Tree (S-tree) satisfying the essential constraints. To solve complex problems with minimal overhead and better convergence, Particle Swarm Optimization (PSO) has been widely applied to solve such optimization problems. Data dissemination in DMS poses QoS aware multicast routing as a multi-objective search optimization problem. Conventional Multi-Objective PSO (MOPSO) algorithm has been used to solve multicast routing problem considering multi-objectives i.e., power consumption, delay, and packet loss. However, it consumes more computation power and energy due to iterative searching in complete multidimensional search space and also because of a generic method which converts invalid to valid solutions (S-trees). In this paper, a Forwarding Zone (FZ) enabled MOPSO (FZMOPSO) is proposed which acts in FZ to search the valid solution irrespective of complete search space. An FZ enabled Valid Particle Generator (FZVPG) operator is proposed and integrated with FZMOPSO to convert invalid to valid particles effectively. The performance of FZMOPSO is evaluated considering twelve different network scenarios and the results are compared with several state-of-the-art algorithms. Experimental results evidenced the effectiveness of FZMOPSO.

Collaborative swarm intelligence to estimate PV parameters

To properly evaluate, control and optimize photovoltaic (PV) systems, it is crucial to accurately estimate the equivalent electric circuit parameters from the respective mathematical models that characterize the PV cells or modules behavior. This is currently a hot research topic that has attracted the attention of numerous researchers. In this paper, we propose a new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely. The proposed methodology has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation. This methodology can incorporate several MHs (two or more swarms) with different potentialities, enabling a good balance between diversification and intensification mechanisms. Furthermore, it is able to explore a multidimensional search space in different regions simultaneously. To validate its performance, the proposed methodology was compared with other well-established MHs in several benchmark functions, and used to estimate PV parameters in single and double-diode models in two case studies, the first using standard literature data, and the second using measured data from a real application with and without the occurrence of partial shading. The proposed methodology was able to find highly accurate solutions with reduced computational cost and high reliability. Comparisons with the other MHs demonstrate that the proposed methodology presents a very competitive performance when solving the PV parameter estimation problem.

District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles

Current district energy optimisation depends on perfect foresight. However, we rarely know how the future will transpire when undertaking infrastructure planning. A key uncertainty that has yet to be studied in this context is building-level energy demand. Energy demand varies stochastically on a daily basis, owing to activities and weather. Yet, most current district optimisation models consider only the average demand. Studies that incorporate demand uncertainty ignore the temporal autocorrelation of energy demand, or require a detailed engineering model for which there is no validation against real consumption data. In this paper, we propose a new 3-step methodology for handling demand uncertainty in mixed integer linear programming models of district energy systems. The three steps are: scenario generation, scenario reduction, and scenario optimisation. Our proposed framework is data-centric, based on sampling of historic demand data using multidimensional search spaces. 500 scenarios are generated from the historical demand of multiple buildings, requiring historical data to be nonparametrically sampled whilst maintaining interdependence of hourly demand in a day. Using scenario reduction, we are able to select a subset of scenarios that best represent the probability distribution of our large number of initial scenarios. The scenario optimisation step constitutes minimising the cost of technology investment and operation, where all realisations of demand from the reduced scenarios are probabilistically weighted in the objective function. We applied these three steps to a real district development in Cambridge, UK, and an illustrative district in Bangalore, India.Our results show that the technology investment portfolios derived from our 3-step methodology are more robust in meeting large possible variations in demand than any model optimised independently with a single demand scenario. This increased robustness comes at a higher monetary cost of investment. However, the high investment cost is lower than the highest possible cost when each of the initial 500 scenarios is optimised independently. In both our case studies, building level energy systems are always more robust than district level ones, a result which disagrees with many existing studies. The outcomes enable better examination of district energy systems. In addition, our methodology is compiled as an open-source code that can be applied to optimise existing and future energy masterplans of districts.

Polyhedral Search Space Exploration in the ExaStencils Code Generator

Performance optimization of stencil codes requires data locality improvements. The polyhedron model for loop transformation is well suited for such optimizations with established techniques, such as the PLuTo algorithm and diamond tiling. However, in the domain of our project ExaStencils, stencil codes, it fails to yield optimal results. As an alternative, we propose a new, optimized, multi-dimensional polyhedral search space exploration and demonstrate its effectiveness: we obtain better results than existing approaches in several cases. We also propose how to specialize the search for the domain of stencil codes, which dramatically reduces the exploration effort without significantly impairing performance.

A Real-Time Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles (CAHEVs) Based on Iterative Dynamic Programming

Connected and automated hybrid electric vehicles (CAHEVs) are a potential solution to the future transportation due to their improved fuel economy, reduced emissions, and capability to mitigate congestion and improve safety. The battery thermal management (BTM) in CAHEVs is one of the crucial problems, because the lithium-ion batteries are highly temperature sensitive. Therefore, a practical and energy-efficient BTM strategy is required for both improving the operating temperature of batteries and saving energy. In this study, the dynamic programming (DP) is implemented for a BTM system in CAHEVs for achieving the optimal cooling/heating energy savings for batteries. To enhance the real-time capability, an iterative approach is proposed to approximate the optimum control strategy iteratively in a multidimensional search space. The proposed iterative DP strategy can improve the system performance and energy-efficiency by fully exploiting the future road information in CAHEVs combined with a model predictive control method. The hardware-in-the-loop validation of the proposed strategy is conducted on the UDDS and the WLTC drive cycles based on a Toyota Prius PHEV model. The results demonstrate the feasibility and effectiveness of the proposed BTM strategy that leads to a considerable BTM energy reduction.

A machine learning technique toward generating minimum energy structures of small boron clusters

AbstractThe search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory. Particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing, PSO, and so on, PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm‐intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques.

An algorithm for optimal design and thermomechanical processing of high carbon bainitic steels

In this paper we present a generic framework of a computationally efficient search algorithm to meet multiple objectives on a multi-dimensional search space. The algorithm is integrated with a penalty-based cost function that enables us to retain the interim optimal solutions. The algorithm has been applied to determine the optimal combination of alloying elements and heat treatment conditions to obtain steels with desired material properties, namely hardness, tensile strength and elongation. In this work, the search space is a combination of eight alloying elements and heat treatment conditions (isothermal temperature and time). To evaluate the quality of the alloying elements and heat treatment combinations, the algorithm is equipped with reduced order models for hardness, tensile strength as well as elongation percentage. The algorithm has been validated with respect to several standard test optimisation problems prescribed in the literature. Subsequently, the validated algorithm has been applied to develop optimal design solutions for steels with desired mechanical properties. In doing so, the framework is tested for three different evaluation functions to showcase the ability of the algorithm to obtain solutions meeting the desired targets/constraints.

FLC control for tuning exploration phase in bio-inspired metaheuristic

<p>Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.</p>

A novel algorithm for real-time framework in multiprocessor environment

The objective of the paper is to represent the real-time framework in multiprocessor environment task scheduling process by examining the novel algorithm advanced PSO. The advanced PSO algorithm has the metaphor as the basis to facilitate social interaction, which makes a search on space by making adjustments to the trajectories of individual vectors, referred to "particles" as they are considered as the points that move within the multidimensional search space. This algorithm will reduce the turn-around time, burst time and waiting time for multiprocessor task scheduling, when compared to existing algorithm like first come first served algorithm, shortest job first algorithm and round robin scheduling algorithm. This proposed algorithm is developed and making hardware for tank level water control for single input single output tank system and two input two output tank system. LABVIEW software is used to implement the real-time algorithm in hardware and software. The time taken for searching best position for particles will be executed and compare with all algorithms using bar chart will be given in result.

Sediment sound speed inversion at low frequencies in shallow water

A direct method previously developed for shallow water inversion is presented here along with the assumptions for its convergence and improvements for the solution of a more complex inverse problem. The method estimates sediment sound speed and follows discontinuities as the sediment layer properties change. Sediment thickness is also calculated. The approach is fast in contrast to other techniques that navigate a multi-dimensional search space. Implementation requires low-frequency measurements at a horizontal array and a source anywhere in the water column with only the continuous spectrum available. Several assumptions are made which appear restrictive at first but can be eventually relaxed. Although the approach was first developed considering a constant density profile, we show how realistic density profiles can be considered. [Work supported by ONR.]

Metaheuristics-Assisted Combinatorial Screening of Eu2+-Doped Ca-Sr-Ba-Li-Mg-Al-Si-Ge-N Compositional Space in Search of a Narrow-Band Green Emitting Phosphor and Density Functional Theory Calculations.

A metaheuristics-based design would be of great help in relieving the enormous experimental burdens faced during the combinatorial screening of a huge, multidimensional search space, while providing the same effect as total enumeration. In order to tackle the high-throughput powder processing complications and to secure practical phosphors, metaheuristics, an elitism-reinforced nondominated sorting genetic algorithm (NSGA-II), was employed in this study. The NSGA-II iteration targeted two objective functions. The first was to search for a higher emission efficacy. The second was to search for narrow-band green color emissions. The NSGA-II iteration finally converged on BaLi2Al2Si2N6:Eu2+ phosphors in the Eu2+-doped Ca-Sr-Ba-Li-Mg-Al-Si-Ge-N compositional search space. The BaLi2Al2Si2N6:Eu2+ phosphor, which was synthesized with no human intervention via the assistance of NSGA-II, was a clear single phase and gave an acceptable luminescence. The BaLi2Al2Si2N6:Eu2+ phosphor as well as all other phosphors that appeared during the NSGA-II iterations were examined in detail by employing powder X-ray diffraction-based Rietveld refinement, X-ray absorption near edge structure, density functional theory calculation, and time-resolved photoluminescence. The thermodynamic stability and the band structure plausibility were confirmed, and more importantly a novel approach to the energy transfer analysis was also introduced for BaLi2Al2Si2N6:Eu2+ phosphors.

A Novel Endmember Extraction Method for Hyperspectral Imagery Based on Quantum-Behaved Particle Swarm Optimization

Endmember extraction is one of the most important issues in hyperspectral image analysis. Under the linear mixing model and pure pixel assumption, a number of convex-geometry-based methods have been developed in the past decades. However, these traditional methods generally produce unsatisfactory results since they require the hyperspectral image to have a convex structure and this is not exactly the case with the real image scene. The particle swarm optimization (PSO) algorithm has recently been employed to address the endmember extraction problem, but its performance is limited by the premature convergence and lower precision of the standard PSO, and much effort is required to enhance the optimization result. To address these problems, in this study, a novel quantum-behaved particle swarm optimization (QPSO) algorithm is proposed for hyperspectral endmember extraction. The notable advantages of the proposed method include: 1) a row–column coding approach for the particles is designed to accelerate the optimization process; 2) a cooperative approach is employed to update the particles’ individual and global best positions, in order to help the particles’ optimization behavior in the multidimensional search space; and 3) two kinds of objective functions, namely, maximizing the simplex volume formed by the endmember combination, and minimizing the root-mean-square error between the original image and its remixed image, are incorporated in a sequential optimization approach for the endmember extraction problem, which makes the algorithm robust to outliers at an acceptable time cost. The extensive experimental results prove that QPSO is able to find the optimal endmember combination.

Multifrequency Particle Swarm Optimization for Enhanced Multiresolution GPR Microwave Imaging

An innovative inverse scattering (IS) technique for the simultaneous processing of multifrequency (MF) ground-penetrating radar (GPR) measurements is proposed. The nonlinear IS problem is solved by profitably integrating a customized MF version of the particle swarm optimizer (PSO) within the iterative multiscaling approach (IMSA) to jointly exploit the reduction of the ratio between unknowns and uncorrelated data with a pervasive exploration of the multidimensional search space for minimizing the probability that the solution is trapped into local minima corresponding to false solutions of the problem at hand. Both numerical and experimental test cases are reported to assess the reliability of the MF-IMSA-PSO method toward accurate GPR tomography as well as improvements with respect to the competitive state-of-the-art inversion approaches.

SEOA-based optimal design of analogue CMOS amplifier circuits

This paper presents social emotional optimisation algorithm (SEOA) based optimal designs of two analogue CMOS amplifier circuits. In SEOA, human behaviour is organised for attaining superior position in society. The person with the topmost position in society provides the optimal solution in multi-dimensional search space in this virtual world. To earn the supreme position in society through the assistance and rivalry with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. The transistors' sizes are optimised to minimise the total MOS occupied area with better performance parameters of the circuits. The simulation results prove that the efficiency of the proposed SEOA-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, specifications of design parameters and yields the least MOS area with improved gain and lower power dissipation.

Efficient Situational Scheduling of Graph Workloads on Single-Chip Multicores and GPUs

Situational dynamic changes in graph analytic algorithm implementations give rise to efficiency challenges in concurrent hardware, such as GPUs and large-scale multicores. These performance variations stem from input dependence, such as the density and degree of the graph being processed. Consequently, concurrency control becomes challenging, because the complex data-dependent behavior in these workloads exhibits a range of plausible algorithmic and architectural choices. This article addresses the question of how to efficiently harness the multidimensional search space of such choices for graph analytic workloads in a real-time execution environment. A key insight is that architectural choices are sufficient to yield a concurrency control setting that is comparable to the optimal setup that optimizes both algorithmic and architectural choices. The authors propose a situationally adaptive scheduler (SAS) that learns the architectural choices offline using synthetically generated graphs. SAS-assisted execution in a real-time setup provides geometric performance gains of 40 percent for a large-scale GPU (Nvidia GTX-970), 35 percent for a smaller GPU (Nvidia GTX- 750Ti), and 30 percent for a large-scale multicore (Intel Xeon Phi).

A binary variant of lightning search algorithm: BLSA

Lightning search algorithm (LSA) is a novel nature-inspired optimization algorithm based on the phenomenon of lighting. This optimization algorithm is generalized from the mechanism of step leader propagation. In this study, a variant of LSA for solving binary optimization problems called as binary LSA (BLSA) is presented. It is done by introducing some modification and introducing tangent hyperbolic sigmoid activation function in updating process of the original version of LSA. To evaluate the quality, convergence rate and robustness of the algorithm, the BLSA is tested with a set of well-utilized 24 benchmark functions. Furthermore, a comparative study with other four well-known binary optimization methods is given for validation of the BLSA performance. The results affirm that the proposed BLSA outperforms the other binary optimization algorithms in multidimensional search space in terms of search accuracy and convergence.

The Inverse Phase Stability Problem as a Constraint Satisfaction Problem: Application to Materials Design

In general, the forward phase stability problem consists of mapping thermodynamic conditions (e.g., composition, temperature, pressure) to corresponding equilibrium states. In this paper, we instead focus on the generalized inverse phase stability problem (GIPSP) that deals with mapping a set of phase constitutions to a set of corresponding thermodynamic conditions. Specifically, we define the GIPSP as mapping of sets of phase constitution definitions in a multidimensional phase constitution search space to corresponding ranges of thermodynamic conditions. Mathematically, the solution to the GIPSP corresponds to all solutions to a continuous constraint satisfaction problem (CCSP). We present novel algorithms combining computational thermodynamics, evolutionary computation, and machine learning to approximate solution sets to the GIPSP as a CCSP. Some preliminary examples demonstrating the algorithms are presented. Moreover, the implications of the proposed framework for the larger problem of materials design are discussed, and future work is suggested.

Optimal sizing and design of CMOS analogue amplifier circuits using craziness‐based particle swarm optimization

SummaryThis paper presents the optimal designs of two analogue complementary metal–oxide–semiconductor (CMOS) amplifier circuits, namely differential amplifier with current mirror load and two‐stage operational amplifier. A modified Particle Swarm Optimization (PSO), called Craziness‐based Particle Swarm Optimization (CRPSO) technique is applied to minimize the total MOS area of the designed circuits. CRPSO is a highly modified version of conventional PSO, which adopts a number of random variables and has a better and faster exploration and exploitation capability in the multidimensional search space. Integration of craziness factor in the fundamental velocity term of PSO not only brings diversity in particles but also pledges convergence close to global best solution. The proposed CRPSO‐based circuit optimization technique is reassured to be free from the intrinsic disadvantages of premature convergence and stagnation, unlike Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The simulation results achieved for the two analogue CMOS amplifier circuits establish the efficacy of the proposed CRPSO‐based approach over those of DE, HS, ABC and PSO in terms of convergence haste, design conditions and design goals. The optimally designed analogue CMOS amplifier circuits occupy the least MOS area and show the best performance parameters like gain and power dissipation, in compared with the other reported literature. Copyright © 2016 John Wiley & Sons, Ltd.