A Comparative Study of Evolutionary Algorithm and Swarm Algorithm for Airfoil Shape Optimization Problems

2003 ◽  
Author(s):  
Chee Meng Tan ◽  
T. Ray ◽  
H. M. Tsai
Keyword(s):  
Comparative Study ◽  
Shape Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Swarm Algorithm

Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications

2021 ◽  
Vol 63 (5) ◽  
pp. 448-452
Author(s):  
Dildar Gürses ◽  
Sujin Bureerat ◽  
Sadiq M. Sait ◽  
Ali Rıza Yıldız
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Slime Mold ◽  
Salp Swarm Algorithm ◽  
Marine Predators ◽  
Metaheuristic Methods ◽  
Engineering Design Problems ◽  
Swarm Algorithm

Abstract This paper focuses on a comparision of recent algorithms such as the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm. The slime mold algorithm (SMA) is a recent optimization algorithm. In order to strengthen its exploitation and exploration abilities, in this paper, a new hybrid slime mold algorithm-simulated annealing algorithm (HSMA-SA) has been applied to structural engineering design problems. As a result of the rules and practices that have become mandatory for fuel emissions by international organizations and governments, there is increasing interest in the design of vehicles with minimized fuel emissions. Many scientific studies have been conducted on the use of metaheuristic methods for the optimum design of vehicle components, especially for reducing vehicle weight. With the inspiration obtained from the above-mentioned methods, the HSMA-SA has been studied to solve the shape optimization of a design case to prove how the HSMA-SA can be used to solve shape optimization problems. The HSMA-SA provides better results as an arithmetic optimization algorithm than the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm.

scholarly journals Relaxed gradient projection algorithm for constrained node-based shape optimization

2021 ◽  
Author(s):  
Ihar Antonau ◽  
Majid Hojjat ◽  
Kai-Uwe Bletzinger
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Design Parameters ◽  
Active Set ◽  
Physical Constraints ◽  
Original Algorithm ◽  
Gradient Projection Algorithm ◽  
Non Linear

AbstractIn node-based shape optimization, there are a vast amount of design parameters, and the objectives, as well as the physical constraints, are non-linear in state and design. Robust optimization algorithms are required. The methods of feasible directions are widely used in practical optimization problems and know to be quite robust. A subclass of these methods is the gradient projection method. It is an active-set method, it can be used with equality and non-equality constraints, and it has gained significant popularity for its intuitive implementation. One significant issue around efficiency is that the algorithm may suffer from zigzagging behavior while it follows non-linear design boundaries. In this work, we propose a modification to Rosen’s gradient projection algorithm. It includes the efficient techniques to damp the zigzagging behavior of the original algorithm while following the non-linear design boundaries, thus improving the performance of the method.

scholarly journals Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1092
Author(s):  
Qing Duan ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Xingping Sun ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Chaotic Sequence ◽  
Engineering Optimization ◽  
Exploration And Exploitation ◽  
Salp Swarm Algorithm ◽  
Symmetric Perturbation ◽  
Swarm Algorithm ◽  
Search Capability ◽  
Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm(SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

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