A Hybrid Fuzzy-Crow Optimizer for Unconstrained and Constrained Engineering Design Problems

Constrained search space is considered a problematic field for engineers. It requires finding solutions for problems satisfying a number of predefined constraints while solving uncertain and ambiguous situations that realistic problems exhibit. As all feasible solutions should have degrees of truth to accommodate real design problems. The fuzzy set theory is able to handle uncertainty issues in real-time problems. In this paper, we introduce a hybrid fuzzy-crow framework for providing an optimal design of constrained and unconstrained engineering problems. The fuzzy-crow framework works on an initial population of fuzzy numbers for problem solutions. It benefits Zadeh extension principle for calculating problem fitness functions and constraints in addition to their membership degrees. The main features of the proposed framework are merging the merits of fuzzy logic and crow search optimization. The fuzzified objective and constraints are incorporated to obtain a fine-tuned solution at fast convergence of the non-dominated solutions. The proposed framework was evaluated based on statistical and convergence analysis using 10 benchmark test functions and five constrained engineering problems against some of the state of art. The results indicated the superiority of the proposed framework over the state of art in finding fine-tuned non-dominated optimized solutions in fuzzy search space.