Paper Info
Title
Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems.
Abstract
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.
Year
DOI
Venue
2019
10.1016/j.knosys.2018.11.024
Knowledge-Based Systems
Keywords
Field
DocType
Optimization,Bio-inspired metaheuristics,Industrial problems,Benchmark test problems
Convergence (routing),Mathematical optimization,Computer science,Competitive algorithm,Artificial intelligence,Optimization algorithm,Machine learning,Computational complexity theory,Metaheuristic
Journal
Volume
ISSN
Citations 
165
0950-7051
17
PageRank 
References 
Authors
0.59
34
2
Name
Order
Citations
PageRank
Gaurav Dhiman163632.82
Vijay Kumar222921.59