A Numerical Optimization Algorithm Inspired by the Strawberry Plant

TL;DR

This paper introduces a novel optimization algorithm inspired by strawberry plants, utilizing unique agent behaviors and minimal parameters, demonstrating effectiveness on standard tests and control problems.

Contribution

The paper presents a new nature-inspired optimization algorithm with distinctive agent duplication and movement strategies, requiring only three parameters, and shows its effectiveness on complex problems.

Findings

Outperforms GA and PSO on standard test functions

Effectively solves a complex problem in robust control theory

Requires only three parameters for tuning

Abstract

This paper proposes a new numerical optimization algorithm inspired by the strawberry plant for solving complicated engineering problems. Plants like strawberry develop both runners and roots for propagation and search for water resources and minerals. In these plants, runners and roots can be thought of as tools for global and local searches, respectively. The proposed algorithm has three main differences with the trivial nature-inspired optimization algorithms: duplication-elimination of the computational agents at all iterations, subjecting all agents to both small and large movements from the beginning to end, and the lack of communication (information exchange) between agents. Moreover, it has the advantage of using only three parameters to be tuned by user. This algorithm is applied to standard test functions and the results are compared with GA and PSO. The proposed algorithm is also used to solve an open problem in the field of robust control theory. These simulations show that the proposed algorithm can very effectively solve complicated optimization problems.