In Search of Excellence: SHOA as a Competitive Shrike Optimization Algorithm for Multimodal Problems

This paper proposes the Shrike Optimization Algorithm (SHOA) as a swarm intelligence optimization algorithm. Many creatures, who live in groups and survive for the next generation, randomly search for food; they follow the best one in the swarm, a phenomenon known as swarm intelligence. While swarm-based algorithms mimic the behaviors of creatures, they struggle to find optimal solutions in multi-modal problem competitions. The swarming behaviors of shrike birds in nature serve as the main inspiration for the proposed algorithm. The shrike birds migrate from their territory in order to survive. However, the SHOA replicates the survival strategies of shrike birds to facilitate their living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modeling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019 and 12 from CEC-2022’s most recent test functions, for a total of 41 competitive mathematical test functions and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon ranking sum and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.