Journal
ENGINEERING WITH COMPUTERS
Volume 37, Issue 4, Pages 3167-3203Publisher
SPRINGER
DOI: 10.1007/s00366-020-00989-x
Keywords
Multi-objective optimization; HMOSHSSA; Spotted Hyena Optimizer; Salp Swarm Algorithm; Constrained optimization; Engineering design problems
Ask authors/readers for more resources
The proposed HMOSHSSA algorithm combines the strengths of MOSHO and SSA to effectively explore the search space and achieve faster convergence to the global best solution. Experimental results demonstrate its competitiveness and superiority in terms of convergence speed, search-ability, and accuracy, surpassing other algorithms. This algorithm also shows efficacy in solving real-life multi-objective optimization problems.
This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm (SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available