Performance Assessment of Metaheuristic Algorithms for Structural Optimization Taking into Account the Influence of Control Parameters

2018 ◽  
pp. 93-101
Author(s):  
Wouter Dillen ◽  
Geert Lombaert ◽  
Nathalie Voeten ◽  
Mattias Schevenels
Keyword(s):  
Structural Optimization ◽  
Performance Assessment ◽  
Metaheuristic Algorithms ◽  
Control Parameters

scholarly journals Performance Assessment of Metaheuristic Algorithms for Structural Optimization Taking Into Account the Influence of Algorithmic Control Parameters

2021 ◽  
Vol 7 ◽  
Author(s):  
Wouter Dillen ◽  
Geert Lombaert ◽  
Mattias Schevenels
Keyword(s):  
Structural Optimization ◽  
Performance Assessment ◽  
Control Parameter ◽  
Assessment Method ◽  
Test Problems ◽  
Control Parameters ◽  
Algorithm Performance ◽  
Importance Sampling Techniques ◽  
Measure Of Performance ◽  
Parameter Values

Metaheuristic optimization algorithms are strongly present in the literature on discrete optimization. They typically 1) use stochastic operators, making each run unique, and 2) often have algorithmic control parameters that have an unpredictable impact on convergence. Although both 1) and 2) affect algorithm performance, the effect of the control parameters is mostly disregarded in the literature on structural optimization, making it difficult to formulate general conclusions. In this article, a new method is presented to assess the performance of a metaheuristic algorithm in relation to its control parameter values. A Monte Carlo simulation is conducted in which several independent runs of the algorithm are performed with random control parameter values. In each run, a measure of performance is recorded. The resulting dataset is limited to the runs that performed best. The frequency of each parameter value occurring in this subset reveals which values are responsible for good performance. Importance sampling techniques are used to ensure that inferences from the simulation are sufficiently accurate. The new performance assessment method is demonstrated for the genetic algorithm in matlab R2018b, applied to seven common structural optimization test problems, where it successfully detects unimportant parameters (for the problems at hand) while identifying well-performing values for the important parameters. For two of the test problems, a better solution is found than the best solution reported so far in the literature.

scholarly journals Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm

2021 ◽  
Vol 11 (7) ◽  
pp. 3270
Author(s):  
Sadik Ozgur Degertekin ◽  
Mohammad Minooei ◽  
Lorenzo Santoro ◽  
Bartolomeo Trentadue ◽  
Luciano Lamberti
Keyword(s):  
Structural Optimization ◽  
Large Scale ◽  
Harmony Search ◽  
Metaheuristic Algorithms ◽  
Truss Structures ◽  
Nonlinear Constraints ◽  
Weight Minimization ◽  
Design Variables ◽  
Gradient Based ◽  
Nodal Displacements

Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.

Multiobjective Evolutionary Structural Optimization Using Combined Static/Dynamic Control Parameters

AIAA Journal ◽  
2006 ◽  
Vol 44 (4) ◽  
pp. 794-802 ◽  
Author(s):  
Woo-Young Kim ◽  
Ramana V. Grandhi ◽  
Mark Haney
Keyword(s):  
Structural Optimization ◽  
Dynamic Control ◽  
Control Parameters ◽  
Evolutionary Structural Optimization

scholarly journals Why Tuning the Control Parameters of Metaheuristic Algorithms Is So Important for Fair Comparison?

MENDEL ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 9-16
Author(s):  
Anezka Kazikova ◽  
Michal Pluhacek ◽  
Roman Senkerik
Keyword(s):  
A Priori ◽  
Parameter Tuning ◽  
Parameter Selection ◽  
Metaheuristic Algorithms ◽  
Initial Parameter ◽  
Control Parameters ◽  
Metaheuristic Optimization ◽  
Performance Differences ◽  
Fair Comparison

Although metaheuristic optimization has become a common practice, new bio-inspired algorithms often suffer from a priori ill reputation. One of the reasons is a common bad practice in metaheuristic proposals. It is essential to pay attention to the quality of conducted experiments, especially when comparing several algorithms among themselves. The comparisons should be fair and unbiased. This paper points to the importance of proper initial parameter configurations of the compared algorithms. We highlight the performance differences with several popular and recommended parameter configurations. Even though the parameter selection was mostly based on comprehensive tuning experiments, the algorithms' performance was surprisingly inconsistent, given various parameter settings. Based on the presented evidence, we conclude that paying attention to the metaheuristic algorithm's parameter tuning should be an integral part of the development and testing processes.

scholarly journals Performance Assessment of A Two-Stage Reciprocating Air Compressor

2019 ◽  
Vol 4 (4) ◽  
pp. 74-82
Author(s):  
Ikpobari Amuele Nwakpang ◽  
Barinaadaa Thaddeus Lebele-Alawa ◽  
Barinyima Nkoi
Keyword(s):  
Performance Assessment ◽  
Flow Rate ◽  
Oil And Gas ◽  
Volume Flow Rate ◽  
Volume Flow ◽  
Control Parameters ◽  
Two Stage ◽  
Air Compressor ◽  
Design Specifications ◽  
Discharge Pressure

This paper presents the performance assessment of a two-stage reciprocating air compressor operating at an Oil and Gas Terminal in Rivers State, Nigeria. The main focus was to investigate the effects of control parameters and clogging on the performance of the compressor. Data were obtained from the manufacturer’s manual, field reports and the field operator’s log sheets. Relevant thermodynamic equations were used to determine and analyse appropriate control parameters of the compressor. Data were also analysed using various appropriate compressor equations and a thermodynamic analysis of the compressor was done to evaluate its performance. The outcome of all the analyses showed that the compressor experienced 26% loss or reduction in the volumetric efficiency, 8% loss in the isothermal efficiency, 11.1% loss in volume flow rate and 21% decrease or reduction in the mass flow rate due to clogging when compared with the design specifications. The analysis also showed that the performance of the compressor was affected by several other factors including the climatic and environmental conditions such as the high operating ambient temperature of the inlet air to the compressor. It revealed that the effects of clogging caused a continuous rise in temperature which reduced the discharge pressure, mass and volume flow rates, isothermal and volumetric efficiencies; thereby reducing its performance in comparison with the design specifications. The results further revealed that clogging was a major factor that affected the performance effectiveness of the reciprocating compressor.

scholarly journals A New Design of Metaheuristic Search Called Improved Monkey Algorithm Based on Random Perturbation for Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mustafa Tunay
Keyword(s):  
Optimization Problems ◽  
Random Perturbation ◽  
Optimal Solution ◽  
Metaheuristic Algorithms ◽  
Local Optimum ◽  
Control Parameters ◽  
Metaheuristic Search ◽  
Different Types ◽  
Different Dimensions ◽  
Number Of Iterations

The aim of this paper is to present a design of a metaheuristic search called improved monkey algorithm (MA+) that provides a suitable solution for the optimization problems. The proposed algorithm has been renewed by a new method using random perturbation (RP) into two control parameters (p1 and p2) to solve a wide variety of optimization problems. A novel RP is defined to improve the control parameters and is constructed off the proposed algorithm. The main advantage of the control parameters is that they more generally prevented the proposed algorithm from getting stuck in optimal solutions. Many optimization problems at the maximum allowable number of iterations can sometimes lead to an inferior local optimum. However, the search strategy in the proposed algorithm has proven to have a successful global optimal solution, convergence optimal solution, and much better performance on many optimization problems for the lowest number of iterations against the original monkey algorithm. All details in the improved monkey algorithm have been represented in this study. The performance of the proposed algorithm was first evaluated using 12 benchmark functions on different dimensions. These different dimensions can be classified into three different types: low-dimensional (30), medium-dimensional (60), and high-dimensional (90). In addition, the performance of the proposed algorithm was compared with the performance of several metaheuristic algorithms using these benchmark functions on many different types of dimensions. Experimental results show that the improved monkey algorithm is clearly superior to the original monkey algorithm, as well as to other well-known metaheuristic algorithms, in terms of obtaining the best optimal value and accelerating convergence solution.

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