An Approach of Adjusting the Switch Probability based on Dimension Size: A Case Study for Performance Improvement of the Flower Pollination Algorithm

TL;DR

This paper proposes a dynamic adjustment of switch probability based on dimension size to enhance the performance of the Flower Pollination Algorithm, demonstrating improved results over the original method.

Contribution

It introduces a novel approach to adapt switch probability dynamically according to dimension size, improving the efficiency of the Flower Pollination Algorithm.

Findings

Performance improved over the original algorithm

Dynamic switch probability benefits high-dimensional problems

Method effective across various functions

Abstract

Numerous meta-heuristic algorithms have been influenced by nature. Over the past couple of decades, their quantity has been significantly escalating. The majority of these algorithms attempt to emulate natural biological and physical phenomena. This research concentrates on the Flower Pollination algorithm, which is one of several bio-inspired algorithms. The original approach was suggested for pollen grain exploration and exploitation in confined space using a specific global pollination and local pollination strategy. As a "swarm intelligence" meta-heuristic algorithm, its strength lies in locating the vicinity of the optimum solution rather than identifying the minimum. A modification to the original method is detailed in this work. This research found that by changing the specific value of "switch probability" with dynamic values of different dimension sizes and functions, the outcome was mainly improved over the original flower pollination method.