STOA: A bio-inspired based optimization algorithm for industrial engineering problems

By Gaurav Dhiman, Amandeep Kaur. Engineering applications of artificial intelligence, 2019

439 citations recorded by the source corpus.

Abstract This paper presents a bio-inspired algorithm called Sooty Tern Optimization Algorithm (STOA) for solving constrained industrial problems.The main inspiration of this algorithm is the migration and attacking behaviors of sea bird sooty tern in nature. These two steps are implemented and mathematically modeled to emphasize exploitation and exploration in a given search space. The proposed algorithm is compared with nine well-known bio-inspired algorithms over 44 benchmark test functions. The analysis of convergence behaviors and computational complexity of the proposed algorithm have been evaluated. Furthermore, to demonstrate its applicability it is then employed to solve six constrained industrial applications. The outcomes of experiment reveal that the proposed algorithm is able to solve challenging constrained problems and is very competitive compared with other optimization algorithms.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f9c068b5d0881fa0f7e70bb60e31d02172a70560

Citations in the corpus

• A novel algorithm for global optimization: Rat Swarm Optimizer (2020, neutral): “…Kumar, Garg, Dhiman, & Kumar, 2017; Singh & Dhiman, 2017; Dhiman & Kaur, 2017; Verma, Kaur, Dhiman, & Kaur, 2018; A. Kaur & Dhiman, 2019; Dhiman & Kaur, 2019a; Dhiman & Kumar, 2019c) (Anitha & Kaarthick, 2019). i i i i i i swarm-intelligence i i i Birattari, & Stutzle, 2006), i i i i i…”