a novel hybrid gwo-sca approach for optimization problems

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ID: 157132
2017
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Abstract
Recent trend of research is to hybridize two and several number of variants to find out better quality of solution of practical and recent real applications in the field of global optimization problems. In this paper, a new approach hybrid Grey Wolf Optimizer (GWO) – Sine Cosine Algorithm (SCA) is exercised on twenty-two benchmark test, five bio-medical dataset and one sine dataset problems. Hybrid GWOSCA is combination of Grey Wolf Optimizer (GWO) used for exploitation phase and Sine Cosine Algorithm (SCA) for exploration phase in uncertain environment. The movement directions and speed of the grey wolve (alpha) is improved using position update equations of SCA. The numerical and statistical solutions obtained with hybrid GWOSCA approach is compared with other metaheuristics approaches such as Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Hybrid Approach GWO (HAGWO), Mean GWO (MGWO), Grey Wolf Optimizer (GWO) and Sine Cosine Algorithm (SCA). The numerical and statistical experimental results prove that the proposed hybrid variant can highly be effective in solving benchmark and real life applications with or without constrained and unknown search areas.
Reference Key
singh2017engineeringa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;N. Singh;S.B. Singh
Journal International journal of molecular sciences
Year 2017
DOI
10.1016/j.jestch.2017.11.001
URL
http://www.sciencedirect.com/science/article/pii/S2215098617307279
Keywords
optimization civil engineering (general)

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