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

TL;DR

This paper introduces the Shrike Optimization Algorithm (SHOA), inspired by shrike birds' migration and survival behaviors, demonstrating its effectiveness in solving complex multimodal optimization problems through extensive benchmarking.

Contribution

The paper presents a novel swarm intelligence algorithm based on shrike birds' behaviors, with a detailed mathematical model and extensive benchmarking against well-known test functions and real-world problems.

Findings

SHOA outperforms competitor algorithms on multimodal benchmarks.

SHOA shows significant statistical superiority in handling complex optimization problems.

SHOA achieves better results on real-world engineering problems.

Abstract

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.