Asynchronous Population-Based Hill Climbing Applied to SPICE Model Generation from EM Simulation Data

2017 ◽  
pp. 423-428 ◽  
Author(s):  
Lars Nolle ◽  
Jens Werner
Keyword(s):  
Population Based ◽  
Hill Climbing ◽  
Model Generation ◽  
Simulation Data ◽  
Spice Model ◽  
Em Simulation

Global search in single-solution-based metaheuristics

2020 ◽  
Vol 54 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Najmeh Sadat Jaddi ◽  
Salwani Abdullah
Keyword(s):  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Global Search ◽  
Hill Climbing ◽  
Search Process ◽  
Local Optimum ◽  
Content Type ◽  
Solution Algorithms ◽  
Single Solution

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

scholarly journals A Novel Framework for Ab Initio Coarse Protein Structure Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Sandhya Parasnath Dubey ◽  
S. Balaji ◽  
N. Gopalakrishna Kini ◽  
M. Sathish Kumar
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Search Space ◽  
Population Based ◽  
Hill Climbing ◽  
Superior Performance ◽  
Protein Database ◽  
Fitness Value

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

Automatic ReRAM SPICE Model Generation From Empirical Data for Fast ReRAM-Circuit Coevaluation

2017 ◽  
Vol 25 (6) ◽  
pp. 1821-1830 ◽  
Author(s):  
Jaehyun Seo ◽  
Sangheon Lee ◽  
Kwangmin Kim ◽  
Sooeun Lee ◽  
Hyunsang Hwang ◽  
...  
Keyword(s):  
Empirical Data ◽  
Model Generation ◽  
Spice Model

scholarly journals Recent developments in simulation-driven multi-objective design of antennas

2015 ◽  
Vol 63 (3) ◽  
pp. 781-789
Author(s):  
S. Koziel ◽  
A. Bekasiewicz
Keyword(s):  
Ultra Wideband ◽  
Kriging Interpolation ◽  
High Fidelity ◽  
Simulation Data ◽  
Simulation Accuracy ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Recent Developments ◽  
Response Correction ◽  
Em Simulation

Abstract This paper addresses computationally feasible multi-objective optimization of antenna structures. We review two recent techniques that utilize the multi-objective evolutionary algorithm (MOEA) working with fast antenna replacement models (surrogates) constructed as Kriging interpolation of coarse-discretization electromagnetic (EM) simulation data. The initial set of Pareto-optimal designs is subsequently refined to elevate it to the high-fidelity EM simulation accuracy. In the first method, this is realized point-by-point through appropriate response correction techniques. In the second method, sparsely sampled high-fidelity simulation data is blended into the surrogate model using Co-kriging. Both methods are illustrated using two design examples: an ultra-wideband (UWB) monocone antenna and a planar Yagi-Uda antenna. Advantages and disadvantages of the methods are also discussed.

SiC MOSFET Corner and Statistical SPICE Model Generation

2020 ◽  
Author(s):  
Canzhong He ◽  
James Victory ◽  
Yunpeng Xiao ◽  
Herbert De Vleeschouwer ◽  
Elvis Zheng ◽  
...  
Keyword(s):  
Model Generation ◽  
Spice Model

scholarly journals Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder

1998 ◽  
Vol 6 (1) ◽  
pp. 61-80 ◽  
Author(s):  
Emma Hart ◽  
Peter Ross ◽  
Jeremy Nelson
Keyword(s):  
Traditional Approach ◽  
Real Life ◽  
Population Based ◽  
Hill Climbing ◽  
Distinct Advantage ◽  
Scheduling Problem ◽  
Flexible System ◽  
Real World Problem ◽  
Large Numbers ◽  
Permutation Schedule

This work addresses the real-life scheduling problem of a Scottish company that must produce daily schedules for the catching and transportation of large numbers of live chickens. The problem is complex and highly constrained. We show that it can be successfully solved by division into two subproblems and solving each using a separate genetic algorithm (GA). We address the problem of whether this produces locally optimal solutions and how to overcome this. We extend the traditional approach of evolving a “permutation + schedule builder” by concentrating on evolving the schedule builder itself. This results in a unique schedule builder being built for each daily scheduling problem, each individually tailored to deal with the particular features of that problem. This results in a robust, fast, and flexible system that can cope with most of the circumstances imaginable at the factory. We also compare the performance of a GA approach to several other evolutionary methods and show that population-based methods are superior to both hill-climbing and simulated annealing in the quality of solutions produced. Population-based methods also have the distinct advantage of producing multiple, equally fit solutions, which is of particular importance when considering the practical aspects of the problem.

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