  In this paper, a swarm intelligence optimization algorithm is proposed as the
Shrike Optimization Algorithm (SHOA). Many creatures living in a group and
surviving for the next generation randomly search for food; they follow the
best one in the swarm, called swarm intelligence. Swarm-based algorithms are
designed to mimic creatures' behaviours, but in multimodal problem competition,
they cannot find optimal solutions in some difficult cases. The main
inspiration for the proposed algorithm is taken from the swarming behaviours of
shrike birds in nature. The shrike birds are migrating from their territory to
survive. However, the SHOA mimics the surviving behaviour of shrike birds for
living, adaptation, and breeding. Two parts of optimization exploration and
exploitation are designed by modelling 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 most recent test functions, a total of 41 competitive
mathematical test functions benchmarked and four real-world engineering
problems with different conditions, both constrained and unconstrained. The
statistical results obtained from the Wilcoxon sum ranking 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.
