Dynamic Solution Probability Acceptance within the Flower Pollination Algorithm for t-way Test Suite Generation

TL;DR

This paper introduces imFPA, an improved Flower Pollination Algorithm with dynamic solution probability, which enhances search diversification and intensification for t-way test suite generation, demonstrating competitive results.

Contribution

The paper proposes a novel dynamic probability mechanism within FPA, improving its effectiveness for combinatorial test suite generation.

Findings

imFPA outperforms existing strategies in t-way test suite generation

Dynamic solution probability enhances search diversification

Experimental results show competitive performance of imFPA

Abstract

Flower Pollination Algorithm (FPA) is the new breed of metaheuristic for the general optimization problem. In this paper, an improved algorithm based on Flower Pollination Algorithm (FPA), called imFPA, has been proposed. In imFPA, the static selection probability is replaced by the dynamic solution selection probability in order to enhance the diversification and intensification of the overall search process. Experimental adoptions on combinatorial t- way test suite generation problem (where t indicates the interaction strength) show that imFPA produces very competitive results as compared to existing strategies.