Solving structural optimization problems with discrete variables using interactive fuzzy search algorithm


Mortazavi A.

STRUCTURAL ENGINEERING AND MECHANICS, vol.79, no.2, pp.247-265, 2021 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 79 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.12989/sem.2021.79.2.247
  • Title of Journal : STRUCTURAL ENGINEERING AND MECHANICS
  • Page Numbers: pp.247-265
  • Keywords: fuzzy logic, hybrid methods, metaheuristic methods, structural optimization, PARTICLE SWARM OPTIMIZATION, TRUSS STRUCTURES, OPTIMAL-DESIGN, DIFFERENTIAL EVOLUTION, TOPOLOGY OPTIMIZATION, WEIGHT MINIMIZATION, FRAME STRUCTURES, STRATEGIES

Abstract

The current investigation deals with assessing the search performance of a recently developed, parameter-free, and self-adaptive search algorithm so-called Interactive Fuzzy Search Algorithm (IFSA) in solving weight minimization of the constrained structural optimization problems with discrete variables. The proposed IFSA combines the navigation pattern of the Interactive Search Algorithm (ISA) with the decision-making competence of fuzzy reasoning. The fuzzy module of the proposed IFSA permanently monitors the search process and adjusts each agent's search behavior by considering the governing condition of the current problem. In structural optimization, due to construction limitations, it is more realistic to select the sizing variables from a discrete domain. Thus, in this study, to empirically evaluate the search capability of the IFSA, it is applied to solve a suite of structural optimization problems with the discrete design variables. The attained outcomes are compared with the ISA and some other related methods addressed in the relevant literature. The acquired accuracy level and demanded number of objective function evaluations indicates that the IFSA, comparatively, using lower computational cost could found lighter structural systems. Also, the comparison of the attained standard deviation values shows that the IFSA demonstrates higher stability during the optimization process. These superior outcomes designate that the fuzzy decision-making mechanism of the IFSA could work properly in dynamically adapting the search behavior of the algorithm with the governing condition of the current problem. Consequently, the promising gained results reveal that IFSA can effectively be applied in solving the structural optimization problems with discrete search domains.