Flower Pollination Algorithm with Linearly Varying Exploration: A Novel Approach for Continuous Function Optimization Problem

Abstract

The Flower Pollination Algorithm (FPA) a recently introduced bio–inspired algorithm based on the natural process of pollination of flowers. FPA has quickly drawn the attention of the research community due to its nature of both exploitation and exploration. In this paper we have introduced a novel variation in its mutation operation by linearly varying the value of its proximate probability parameter. We have evaluated the proposed algorithm on a number of benchmark problems and the experimental results are compared with the basic FPA. The empirical study indicates that the proposed FPA with variable exploration rate has better optimization performance than the basic FPA algorithm on most of the benchmark functions.