A novel hybrid GWO-SCA approach for optimization problems

TLDR

Recent research trends hybridize multiple algorithms to improve solution quality for practical global optimization problems. The paper proposes a hybrid GWO–SCA algorithm tested on 22 benchmark, 5 biomedical, and 1 sine dataset problems. The hybrid GWOSCA combines GWO for exploitation and SCA for exploration, updating the grey wolf’s movement with SCA position equations, and its performance is compared against PSO, ALO, WOA, HAGWO, MGWO, GWO, and SCA. Experimental results show the hybrid variant is highly effective for benchmark and real‑life applications, even with constrained or unknown search spaces.

Abstract

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.