Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry

2021 ◽  
Vol 63 (4) ◽  
pp. 356-359
Author(s):  
Betül Sultan Yıldız ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Mehmet Umut Erdaş ◽  
Ali Rıza Yıldız ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Methods ◽  
The Political ◽  
Lévy Flight ◽  
Levy Flight ◽  
Industrial Companies ◽  
And Topology ◽  
Product Costs

Abstract This article focuses on minimizing product costs by using the newly developed political optimization algorithm (POA), the Archimedes ‘optimization algorithm(AOA), and the Levy flight algorithm(LFA) in product development processes. Three structural optimization methods, size optimization, shape optimization, and topology optimization, are extensively applied to create inexpensive structures and render designs efficient. Using size, shape, and topology optimization in an integrated way, It is possible to obtain the most efficient structures in industry. The political optimization algorithm (POA) is a metaheuristic algorithm that can be used to solve many optimization problems. This study investigates the search capability and computational efficiency of POA for optimizing vehicle structures. By examining the results obtained, we prove the apparent superiority of the POA to other recent famous metaheuristics such as the Archimedes optimization algorithm and the Levy flight algorithm. The most important result of this paperwill be to provide an impressive aid for industrial companies to fill the gaps in their product design stages.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.

Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems

2021 ◽  
Vol 63 (6) ◽  
pp. 547-551
Author(s):  
Mustafa Yıldız ◽  
Natee Panagant ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Sadiq M. Sait ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Lévy Flight ◽  
Levy Flight ◽  
Design Variables ◽  
Lion Algorithm ◽  
Mass Minimization ◽  
Meta Modeling ◽  
Ant Lion

Abstract The Lévy flight distribution optimization algorithm is a recently developed meta-heuristic. In this study, the Lévy flight distribution optimization algorithm and the Taguchi method are hybridized to solve the shape optimization problem, which is the final step in developing optimum structural components. The new method is termed the hybrid Lévy flight distribution and Taguchi (HLFD-T) algorithm. Geometric dimensions are used as design variables in the optimization, and the problem is aimed at mass minimization. The constraint in the problem is the maximum stress value. The well-known Kriging meta-modeling approach and a specifically developed hybrid approach have been coupled in this paper to find the component’s optimal geometry. The results show that the proposed hybrid algorithm (HLFD-T) has more robust features than the ant lion algorithm, the whale algorithm, and the Lévy flight distribution optimization algorithm for obtaining an optimal component geometry.

scholarly journals An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Feng Qian ◽  
Mohammad Reza Mahmoudi ◽  
Hamïd Parvïn ◽  
Kim-Hung Pho ◽  
Bui Anh Tuan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Global Optimum ◽  
Initial Population ◽  
Swarm Optimization ◽  
Conventional Optimization

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

Non-Probabilistic Based Topology Optimization Under External Load Uncertainty With Eigenvalue-Superposition of Convex Models

2013 ◽  
Author(s):  
Xike Zhao ◽  
Hae Chang Gea ◽  
Wei Song
Keyword(s):  
Topology Optimization ◽  
External Load ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Convex Model ◽  
Structure Response ◽  
Gradient Based ◽  
Bounded Convex ◽  
Topology Optimization Method

In this paper the Eigenvalue-Superposition of Convex Models (ESCM) based topology optimization method for solving topology optimization problems under external load uncertainties is presented. The load uncertainties are formulated using the non-probabilistic based unknown-but-bounded convex model. The sensitivities are derived and the problem is solved using gradient based algorithm. The proposed ESCM based method yields the material distribution which would optimize the worst structure response under the uncertain loads. Comparing to the deterministic based topology optimization formulation the ESCM based method provided more reasonable solutions when load uncertainties were involved. The simplicity, efficiency and versatility of the proposed ESCM based topology optimization method can be considered as a supplement to the sophisticated reliability based topology optimization methods.

scholarly journals Multiverse Optimization Algorithm Based on Lévy Flight Improvement for Multithreshold Color Image Segmentation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32805-32844 ◽  
Author(s):  
Heming Jia ◽  
Xiaoxu Peng ◽  
Wenlong Song ◽  
Chunbo Lang ◽  
Zhikai Xing ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Lévy Flight ◽  
Levy Flight
Sign in / Sign up

Export Citation Format

Share Document