scholarly journals The multilevel ant stigmergy algorithm for numerical optimization

2006 ◽  
Vol 19 (2) ◽  
pp. 247-260 ◽  
Author(s):  
Peter Korosec ◽  
Jurij Silc
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Techniques ◽  
Industrial Case Study ◽  
New Approach ◽  
Collective Work ◽  
Better Than

The Multilevel Ant Stigmergy Algorithm (MASA) is a new approach to solving multi-parameter problems based on stigmergy, a type of collective work that can be observed in nature. In this paper we evaluate the performance of MASA regarding its applicability as numerical optimization techniques. The evaluation is performed with several widely used benchmarks functions, as well as on an industrial case study. We also compare the MASA with Differential Evolution, well-known numerical optimization algorithm. The average solution obtained with the MASA was better than a solution recently found using Differential Evolution.

Assessment of the use of n-alkanes as markers to describe the complex diets of herbivores

2002 ◽  
Vol 138 (4) ◽  
pp. 425-434 ◽  
Author(s):  
H. MARTINS ◽  
D. A. ELSTON ◽  
R. W. MAYES ◽  
J. A. MILNE
Keyword(s):  
Least Squares ◽  
Plant Species ◽  
Optimization Algorithm ◽  
Diet Composition ◽  
Diet Selection ◽  
Optimization Techniques ◽  
Wild Rabbit ◽  
Sensitivity Tests ◽  
The Individual

Previous approaches to the description of complex diets, based on n-alkanes and optimization techniques, have grouped the plant species to reduce the number of components. Diet estimates have been obtained with least-squares routines by minimizing the discrepancy between faecal alkane concentrations calculated from herbage concentrations and actual faecal alkane concentrations. The effect of diet selection within groups can only be assessed by using sensitivity tests or by giving subjective weights to the individual plants. In the current study, a new optimization algorithm was developed that selects weightings that lead to consistent estimates of group proportions. The diet of the wild rabbit in a southern Portuguese montado was used as a case study. Estimates of the diet composition obtained using the new algorithm were compared with those of a conventional routine. The new algorithm was shown to provide, on average, more accurate estimates of the proportions of the groups in the diet. The effect of grouping plant species according to criteria other than similarity in n-alkane pattern on the accuracy of estimates was shown to be non-significant.

scholarly journals An α-Model Parametrization Algorithm for Optimization with Differential-Algebraic Equations

2022 ◽  
Vol 12 (2) ◽  
pp. 890
Author(s):  
Paweł Dra̧g
Keyword(s):  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Differential Algebraic Equations ◽  
Multiple Shooting ◽  
Small Range ◽  
Algebraic Equations ◽  
New Approach ◽  
Highly Nonlinear ◽  
Special Cases ◽  
Wide Range

An optimization task with nonlinear differential-algebraic equations (DAEs) was approached. In special cases in heat and mass transfer engineering, a classical direct shooting approach cannot provide a solution of the DAE system, even in a relatively small range. Moreover, available computational procedures for numerical optimization, as well as differential- algebraic systems solvers are characterized by their limitations, such as the problem scale, for which the algorithms can work efficiently, and requirements for appropriate initial conditions. Therefore, an αDAE model optimization algorithm based on an α-model parametrization approach was designed and implemented. The main steps of the proposed methodology are: (1) task discretization by a multiple-shooting approach, (2) the design of an α-parametrized system of the differential-algebraic model, and (3) the numerical optimization of the α-parametrized system. The computations can be performed by a chosen iterative optimization algorithm, which can cooperate with an outer numerical procedure for solving DAE systems. The implemented algorithm was applied to solve a counter-flow exchanger design task, which was modeled by the highly nonlinear differential-algebraic equations. Finally, the new approach enabled the numerical simulations for the higher values of parameters denoting the rate of changes in the state variables of the system. The new approach can carry out accurate simulation tests for systems operating in a wide range of configurations and created from new materials.

scholarly journals Towards a Flexible Smart Factory with a Dynamic Resource Orchestration

2021 ◽  
Vol 11 (17) ◽  
pp. 7956
Author(s):  
Milan Pisarić ◽  
Vladimir Dimitrieski ◽  
Marko Vještica ◽  
Goran Krajoski ◽  
Mirna Kapetina
Keyword(s):  
Production Process ◽  
Industrial Revolution ◽  
Smart Factory ◽  
Production Lines ◽  
Industrial Case Study ◽  
New Approach ◽  
Software Product ◽  
Resource Orchestration ◽  
Dynamic Resource

Amid the current industrial revolution, a total disruption of the existing production lines may seem to be the easiest approach, as the potential possibilities seem limitless when starting from the ground up. On the business side, an adaptation of existing production lines is always a preferred option. In support of adaptation as opposed to disruption, this paper presents a new approach of using production process orchestration in a smart factory, discussed in an industrial case-study example. A proposed smart factory has the Orchestrator component in its core, responsible for complete semantical orchestration of production processes on one hand, and various factory resources on the other hand, in order to produce the desired product. The Orchestrator is a complex, modular, highly scalable, and pluggable software product responsible for automatised planning, scheduling, and execution of the complete production process. According to their offered capabilities, non-smart and smart resources—machines, robots, humans—are simultaneously and dynamically assigned to execute their dedicated production steps.

scholarly journals Lost In Translation: Exposing Hidden Compiler Optimization Opportunities

2020 ◽  
Author(s):  
Kyriakos Georgiou ◽  
Zbigniew Chamski ◽  
Andres Amaya Garcia ◽  
David May ◽  
Kerstin Eder
Keyword(s):  
Compiler Optimization ◽  
Optimization Techniques ◽  
Raspberry Pi ◽  
Routine Testing ◽  
New Approach ◽  
Development Cycle ◽  
Iterative Compilation ◽  
Dependent Potential ◽  
Lost In Translation

Abstract Existing iterative compilation and machine learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler’s optimizer as part of the compiler’s daily development cycle. In this paper, we first establish the required properties that a technique must exhibit to enable such tuning. We then introduce an enhancement to the classic nightly routine testing of compilers, which exhibits all the required properties and thus is capable of driving the improvement and tuning of the compiler’s common optimizer. This is achieved by leveraging resource usage and compilation information collected while systematically exploiting prefixes of the transformations applied at standard optimization levels. Experimental evaluation using the LLVM v6.0.1 compiler demonstrated that the new approach was able to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U and the Arm Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+. As a case study, we demonstrate how the insights from our approach enabled us to identify and remove a significant shortcoming of the CFG simplification pass of the LLVM v6.0.1 compiler.

scholarly journals Strengthened Teaching–Learning-Based Optimization Algorithm for Numerical Optimization Tasks

2021 ◽  
Author(s):  
Xuefen Chen ◽  
Chunming Ye ◽  
Yang Zhang ◽  
Lingwei Zhao ◽  
Jing Guo ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Techniques ◽  
Data Availability ◽  
Search Performance ◽  
Local Optima ◽  
Cauchy Mutation ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Solution Accuracy

Abstract The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

scholarly journals Novelty Ranking Approach with Z-Score and Fuzzy Multi- Attribute Decision Making Combination

2018 ◽  
Vol 7 (4.7) ◽  
pp. 476
Author(s):  
Basri . ◽  
Syarli .
Keyword(s):  
Decision Making ◽  
Statistical Technique ◽  
Z Score ◽  
New Approach ◽  
Score Method ◽  
Multi Attribute Decision Making ◽  
Weighted Data ◽  
Hierarchy Process ◽  
Better Than

This study aims to recommend a new approach in the ranking system by analyzing the combination of the Z-Score method and the Fuzzy Multi-Attribute Decision Making (FMADM) method. This fusion is based on the merging of the advantages of Z-Score and FMADM as a superiority method in statistical rank data processing with weighting data distribution. The lack of Z-Score in processing multi-attributes weighted data can be improved by the FMADM method. In this study, the integration of the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods was used as the FMADM method with the Z-Score statistical technique. The results of the analysis in the case study show that the integration of the Z-Score and AHP-Weighted Product (Z-WeP) methods can provide maximum results with similarities to the Z-Score results by 86%. Analysis of criterion values on alternatives also shows that Z-WeP can work better than some other of FMADM approaches.   

scholarly journals Data-Driven Engineering – Definitions and Insights from an Industrial Case Study for a New Approach in Technical Product Development

2020 ◽  
Author(s):  
Jakob Trauer ◽  
Sebastian Schweigert-Recksiek ◽  
Luis Onuma Okamoto ◽  
Karsten Spreitzer ◽  
Markus Mörtl ◽  
...  
Keyword(s):  
Product Development ◽  
Data Driven ◽  
Industrial Case Study ◽  
New Approach ◽  
Technical Product
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