ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems

In this paper, a hybrid bio-inspired metaheuristic optimization approach namely emperor penguin and salp swarm algorithm (ESA) is proposed. This algorithm imitates the huddling and swarm behaviors of emperor penguin optimizer and salp swarm algorithm, respectively. The efficiency of the proposed ESA is evaluated using scalability analysis, convergence analysis, sensitivity analysis, and ANOVA test analysis on 53 benchmark test functions including classical and IEEE CEC-2017. The effectiveness of ESA is compared with well-known metaheuristics in terms of the optimal solution. The proposed ESA is also applied on six constrained and one unconstrained engineering problems to evaluate its robustness. The results reveal that ESA offers optimal solutions as compared to the other competitor algorithms.

Automated summary

In this paper, a hybrid bio-inspired metaheuristic optimization approach named Emperor Penguin and Salp Swarm Algorithm (ESA) is proposed. The efficiency of the ESA is evaluated through various analyses on 53 benchmark test functions, showing that it offers optimal solutions compared to other competitor algorithms. The robustness of ESA is also demonstrated through its application on six constrained and one unconstrained engineering problems.