Purpose
This paper aims to present a new hybrid algorithm of Particle Swarm Optimization and the Genetic Algorithm (PSOGA) to optimize the space trusses with continuous design variables. The PSOGA is an efficient hybridized algorithm to solve optimization problems.
Design/methodology/approach
These algorithms have shown outstanding performance in solving optimization problems with continuous variables. The PSO conceptually models the social behavior of birds, in which individual birds exchange information about their position, velocity and fitness. The behavior of a flock is influencing the probability of migration to other regions with high fitness. The GAs procedure is based on the mechanism of natural selection. The present study uses mutation, random selection and reproduction to reach the best genetic algorithm by the operators of natural genetics. Thus, only identical chromosomes or particles can be converged.
Findings
In this research, using the idea of hybridization PSO and GA algorithms are hybridized and a new meta-heuristic algorithm is developed to minimize the space trusses with continuous design variables. To showing the efficiency and robustness of the new algorithm, several benchmark problems are solved and compared with other researchers.
Originality/value
The results indicate that the hybrid PSO algorithm improved in both exploration and exploitation. The PSO algorithm can be used to minimize the weight of structural problems under stress and displacement constraints.
The Chaos Game Optimization (CGO) algorithm is a recently-developed metaheuristic inspired by chaos theory and fractal configurations. In CGO, possible optimal solutions are defined as seeds and the searching process is performed using some simple equations. In this paper, Weighted Chaos Game Optimization (WCGO) is proposed and implemented to optimize engineering structures with dynamic constraints. In this method, an inertia weight coefficient based on the minimum and maximum values of the objective function is introduced to create a better balance between exploration and exploitation during the searching process. By applying the inertia weight coefficient to the seeds, their positions can be controlled accurately. To evaluate the performance of WCGO, a wide range of mathematical benchmark functions, as well as several structural design optimization problems under dynamic constraints, are computationally investigated using the new algorithm. In order to demonstrate the efficiency and robustness of WCGO, its results have been compared with those obtained by some conventional methods from the literature. Additionally, a Friedman rank test is conducted to perform a statistical study on the performance of the considered algorithms. The findings indicate that WCGO performs better than its rivals in solving these structural optimization problems with dynamic constraints.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.