Optimization of engineering problems with discrete and continuous constraints using dynamic adaptive meta-heuristic algorithms

علوم کاربردی و محاسباتی در مکانیک

Article 4, Volume 37, Issue 1 - Serial Number 39, April 2025, Pages 53-70   

Document Type: Original Article

DOI: 10.22067/jacsm.2024.86041.1232

Authors

sayedMostafa mousavi¹; Alireza Toloei^{* 1}; mohammadhosein Toloei²

¹Shahid Beheshti University of Tehran, Tehran.

²Tehran University of Arts, Tehran.

Abstract

The purpose of this article is to implement different methods of meta-heuristic algorithms to solve five engineering problems. these engineering problems have been optimized using five meta-heuristic algorithms of firefly, colonial competition, frog, ants and gray wolf with the aim of reducing the costs of engineering problems. in each of the algorithms, a dynamic adaptive factor is introduced to balance the convergence rate and absolute optimal search ability by adjusting the search speed during the search process. Investigations show that in each of the algorithms techniques are used to leave the local optimum, which makes the answers converge to the absolute optimum. To evaluate the quality and accuracy of the algorithms, the sensitivity test and the comparison of the convergence numbers for the results of the implementation of each algorithm on the data have been used. The results show that the firefly algorithm in spring tension problem, frog algorithm in three-bar truss problem, the colonial competition algorithm in the speed reducer and gear design problems, and the gray wolf algorithm in the pressure tank problem provided more accurate performance in finding the absolute optimum. . In fact, these algorithms make it easy to achieve the optimal solution by generating a random population, creating a neighborhood and choosing the best neighbor, provided that the constraints of the variables of the problem are satisfied. As a result, this paper shows that any meta-heuristic algorithm can perform better in a specific engineering problem, depending on the type of problem and environmental conditions.

Keywords

Constrained engineering design optimization problems; continuous and discrete design variables; meta-heuristics; Imperialist Competitive; ants; Frog; grey wolf; firefly

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