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

• Published in: Engineering applications of artificial intelligence Vol. 82; pp. 148 - 174
• Main Authors: Dhiman, Gaurav, Kaur, Amandeep
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2019
• Subjects: Benchmark test problems; Bio-inspired metaheuristic techniques; Constrained problems; Optimization; Constrained problems; Bio-inspired metaheuristic techniques; Benchmark test problems; Optimization
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2019.03.021

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. •A novel metaheuristic named Sooty Tern Optimization Algorithm is proposed.•The proposed algorithm is tested on 44 benchmark test functions.•The proposed algorithm is capable for exploration, exploitation, and local optima avoidance.•The results of engineering problems prove the applicability in real-life applications.