Interactive fuzzy search algorithm: A new self-adaptive hybrid optimization algorithm

Current investigation deals with presenting a new hybrid metaheuristic optimization technique which is named as Interactive Fuzzy Search Algorithm (IFSA). Proposed method combines the affirmative features of Integrated Particle Swarm Optimizer (iPSO) and Teaching and Learning Based Optimizer (TLBO) techniques with a fuzzy decision mechanism. Proposed IFSA benefiting its fuzzy module provides a self-adaptive synchronization between local and global search strategies during the optimization process. Since in the proposed approach the tunable parameters are automatically assigned, IFSA acts as an ad-hoc free algorithm. To authenticate the validity of the proposed method, its performance is verified over the number of different types of mathematical, mechanical and structural optimization problems. Achieved outcomes indicate that the introduced algorithm has a high competitive potential on solving different class of optimization problems. © 2019 Elsevier Lt