Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

• Published in: Knowledge-based systems Vol. 165; pp. 169 - 196
• Main Authors: Dhiman, Gaurav, Kumar, Vijay
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2019; Elsevier Science Ltd
• Subjects: Algorithms; Benchmark test problems; Bio-inspired metaheuristics; Heuristic; Industrial applications; Industrial engineering; Industrial problems; Migration; Optimization; Optimization algorithms; Bio-inspired metaheuristics; Benchmark test problems; Optimization; Industrial problems
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2018.11.024

This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.