scholarly journals Marine predators' algorithm: Application in applied mechanics

Tehnika ◽  
2021 ◽  
Vol 76 (5) ◽  
pp. 613-620
Author(s):  
Branislav Milenković ◽  
Mladen Krstić
Keyword(s):  
Cantilever Beam ◽  
Pressure Vessel ◽  
Optimization Problems ◽  
Applied Mechanics ◽  
Software Suite ◽  
Marine Predators ◽  
Engineering Problems ◽  
Speed Reducer

In this paper we will demonstrate how Marine Predators Algorithm (MPA for short) can be used for solving certain optimization problems in applied mechanics. In the first part, biological fundamentals, as well as method explanation are given. Afterwards, MPA algorithm and its ' applicability is explained in detail. The pseudo code for this algorithm was written using Matlab R2019a software suite. This algorithm can be used for optimization o f engineering problems, such as: pressure vessel optimization, cantilever beam optimization, cone clutch optimization and speed reducer optimization. In the end, all the results for the fore mentioned problems, as well as a result comparison with other methods are shown.

scholarly journals Implementation of Harris Hawks Optimization (HHO) algorithm to solve engineering problems

Tehnika ◽  
2021 ◽  
Vol 76 (4) ◽  
pp. 439-446
Author(s):  
Branislav Milenković
Keyword(s):  
Cantilever Beam ◽  
Pressure Vessel ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Metaheuristic Algorithms ◽  
Software Suite ◽  
Compression Spring ◽  
Engineering Problems ◽  
Speed Reducer ◽  
Statistical Results

Recently, optimization techniques have become very important and popular in different engineering applications. In this paper we demonstrate how Harris Hawks Optimization (HHO) algorithm can be used to solve certain optimization problems in engineering. In the second part, biological fundamentals, as well as method explanation are given. Afterwards, the HHO algorithm and its' applicability is explained in detail. The pseudo code for this algorithm was written using MATLAB R2019a software suite. Harris Hawks Optimization (HHO) algorithm was used for optimization of engineering problems, such as: speed reducer optimization, pressure vessel optimization, cantilever beam optimization and tension/compression spring optimization. The statistical results and comparisons show that the HHO algorithm provides very promising and competitive results compared to others metaheuristic algorithms.

scholarly journals APPLICATION OF THE METAHEURISTIC TUNICATE SWARM ALGORITHM IN SOLVING APPLIED MECHANICS PROBLEMS

2021 ◽  
Vol 24 (2) ◽  
pp. 31-34
Author(s):  
Mladen Krstić ◽  
◽  
Branislav Milenković ◽  
Đorđe Jovanović ◽  
◽  
...  
Keyword(s):  
Cantilever Beam ◽  
Three Dimensional ◽  
Metaheuristic Algorithm ◽  
Applied Mechanics ◽  
Comparison Of Results ◽  
Swarm Behavior ◽  
Speed Reducer ◽  
Swarm Algorithm

In this paper, the principles of a metaheuristic algorithm based on tunicate swarm behavior are shown. The Tunicate Swarm Algorithm (TSA for short) was used for solving problems in applied mechanics (speed reducer, cantilever beam and three-dimensional beam optimization). In the end, a comparison of results obtained by TSA and results obtained by other methods is given.

scholarly journals Application of grey wolf algorithm for solving engineering optimization problems

Tehnika ◽  
2021 ◽  
Vol 76 (1) ◽  
pp. 50-57
Author(s):  
Branislav Milenković ◽  
Mladen Krstić ◽  
Đorđe Jovanović
Keyword(s):  
Optimization Problems ◽  
Side Impact ◽  
Engineering Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Engineering Problems ◽  
Speed Reducer ◽  
It Application ◽  
Grey Wolf Algorithm ◽  
Engineering Optimization Problems

This paper presents grey wolf optimization - GWO. After presenting the biological basis of GWO, it explains the method itself and then the main algorithms of the GWO method as well as their mathematical models. The Grey Wolf Algorithm (GWO) is presented in detail as well as the manner of its operation and it application to optimization examples of engineering problems, such as: optimization of speed reducer, pressure vessel, spring, car side impact, cone coupling and cantilever beam. At the end, the results obtained by the GWO method are compared to the results previously obtained by other methods.

Software suite for determining the tilts of tower-type structures according to terrestrial laser scanning

2020 ◽  
Vol 961 (7) ◽  
pp. 2-7
Author(s):  
A.V. Zubov ◽  
N.N. Eliseeva
Keyword(s):  
Laser Scanning ◽  
Optimization Problems ◽  
Optimization Methods ◽  
User Participation ◽  
Software Suite ◽  
Current State ◽  
Visual Basic For Applications ◽  
Operational Data ◽  
Theoretical Developments

The authors describe a software suite for determining tilt degrees of tower-type structures according to ground laser scanning indication. Defining the tilt of the pipe is carried out with a set of measured data through approximating the sections by circumferences. They are constructed using one of the simplest search engine optimization methods (evolutionary algorithm). Automatic filtering the scan of the current section from distorting data is performed by the method of assessing the quality of models constructed with that of least squares. The software was designed using Visual Basic for Applications. It contains several blocks (subprograms), with each of them performing a specific task. The developed complex enables obtaining operational data on the current state of the object with minimal user participation in the calculation process. The software suite is the result of practical implementing theoretical developments on the possibilities of using search methods at solving optimization problems in geodetic practice.

Approximate Solutions to a Cantilever Beam Using Optimal Homotopy Asymptotic Method

2013 ◽  
Vol 430 ◽  
pp. 22-26 ◽  
Author(s):  
Vasile Marinca ◽  
Nicolae Herisanu ◽  
Traian Marinca
Keyword(s):  
Small Parameter ◽  
Cantilever Beam ◽  
Asymptotic Method ◽  
Approximate Solutions ◽  
Lumped Mass ◽  
Engineering Problems ◽  
Approximate Frequency ◽  
Optimal Homotopy Asymptotic Method ◽  
Analytical Expressions

The response of a cantilever beam with a lumped mass attached to its free end subject to harmonical excitation at the base is investigated by means of the Optimal Homotopy Asymptotic Method (OHAM). Approximate accurate analytical expressions for the solutions and for approximate frequency are determined. This method does not require any small parameter in the equation. The obtained results prove that our method is very accurate, effective and simple for investigation of such engineering problems.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

An enhanced surrogate-assisted differential evolution for constrained optimization problems

2021 ◽  
Author(s):  
Rafael de Paula Garcia ◽  
Beatriz Souza Leite Pires de Lima ◽  
Afonso Celso de Castro Lemonge ◽  
Breno Pinheiro Jacob
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Optimization Procedure ◽  
Nearest Neighbors ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Constrained Optimization Problems ◽  
Engineering Problems ◽  
Complex Engineering

Abstract The application of Evolutionary Algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate-assisted evolutionary optimization procedure is proposed. The procedure combines the Differential Evolution method with a Anchor -nearest neighbors ( –NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

scholarly journals Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

2020 ◽  
Vol 10 (14) ◽  
pp. 4821
Author(s):  
Yong Zhang ◽  
Pengfei Wang ◽  
Liuqing Yang ◽  
Yanbin Liu ◽  
Yuping Lu ◽  
...  
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Local Development ◽  
Anas Platyrhynchos ◽  
Intelligent Algorithm ◽  
Intelligence Algorithm ◽  
Engineering Problems ◽  
The Mathematical Model ◽  
Swarm Intelligence Algorithm

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

scholarly journals Biobjective Optimization Algorithms Using Neumann Series Expansion for Engineering Design

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Huan Guo ◽  
Yoshino Tatsuo ◽  
Lulu Fan ◽  
Ao Ding ◽  
Tianshuang Xu ◽  
...  
Keyword(s):  
Series Expansion ◽  
Optimization Problems ◽  
Neumann Series ◽  
Optimization Strategy ◽  
Weighted Sum Method ◽  
Car Crash ◽  
Engineering Problems ◽  
Biobjective Optimization ◽  
Neumann Series Expansion ◽  
Novel Algorithms

In this paper, two novel algorithms are designed for solving biobjective optimization engineering problems. In order to obtain the optimal solutions of the biobjective optimization problems in a fast and accurate manner, the algorithms, which have combined Newton’s method with Neumann series expansion as well as the weighted sum method, are applied to deal with two objectives, and the Pareto optimal front is achieved through adjusting weighted factors. Theoretical analysis and numerical examples demonstrate the validity and effectiveness of the proposed algorithms. Moreover, an effective biobjective optimization strategy, which is based upon the two algorithms and the surrogate model method, is developed for engineering problems. The effectiveness of the optimization strategy is proved by its application to the optimal design of the dummy head structure in the car crash experiments.

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