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Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems
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DOI：10.1016/j.knosys.2018.11.024.png)
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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. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Optimization
Bio-inspired meta heuristics
Industrial problems
Benchmark test problems
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7.6
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1.2W
被引数:
4.5W
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