Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

Weijia Cui,Yuzhu He

doi:10.1155/2018/6906295

Abstract

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

Highlights

Conventional optimization methods face serious challenges in modern sciences because the characteristics of optimization problems are often noncontinuous, nonlinear, multivariate, or nonconvex [1]
Most swarm intelligence algorithms are developed by simulation of foraging behavior, migration patterns, or the evolutionary approach in natural species, and these algorithms include the genetic algorithm (GA) [2], particle swarm optimization (PSO) [3], differential evolution (DE) [4], shuffled frog leaping algorithm (SFLA) [5], biogeography-based optimization (BBO) [6], cuckoo search (CS) [7], krill herd algorithm (KH) [8], fruit fly optimization (FFO) [9], pigeon inspired optimization (PIO) [10], invasive weed optimization (IWO) [11], and bat algorithm (BA) [12]
In this paper, considering that potentially valuable information might exist in certain dimensions in different candidate solution vectors, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) [24] is developed to obtain more promising candidate solution by combining the useful information among the best individual, current individual, and a random selected individual

Summary

Introduction

Conventional optimization methods face serious challenges in modern sciences because the characteristics of optimization problems are often noncontinuous, nonlinear, multivariate, or nonconvex [1]. In this paper, considering that potentially valuable information might exist in certain dimensions in different candidate solution vectors, an OL strategy based on orthogonal experiment design (OED) [24] is developed to obtain more promising candidate solution by combining the useful information among the best individual, current individual, and a random selected individual. In this way, the convergence accuracy and speed would be significantly improved.

Global Optimization Problems

Flower Pollination Algorithm

Experimental Verification and Comparisons

Findings

Conclusion