# Multi-Species Cuckoo Search Algorithm for Global Optimization

## TL;DR

This paper introduces a multi-species cuckoo search algorithm inspired by co-evolution in nature, demonstrating improved efficiency over existing methods in solving complex nonlinear optimization problems through extensive benchmarks and case studies.

## Contribution

The paper proposes a novel multi-species cuckoo search algorithm that enhances the standard cuckoo search by incorporating multiple interacting species for better optimization performance.

## Key findings

- Outperforms standard cuckoo search and genetic algorithms on benchmark functions.
- Effectively finds optimal solutions in nonlinear, multimodal problems.
- More efficient in most tested cases, showing potential as a versatile optimization tool.

## Abstract

Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms. This work extends the standard cuckoo search (CS) by using the successful features of the cuckoo-host co-evolution with multiple interacting species, and the proposed multi-species cuckoo search (MSCS) intends to mimic the multiple species of cuckoos that compete for the survival of the fittest, and they co-evolve with host species with solution vectors being encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal design case studies in practical applications. Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and in this case all optimal solutions are achievable. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm, which demonstrated the efficiency of the present algorithm. Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases, leading a potentially very effective tool for solving nonlinear optimization problems.