13 Invasive Weed Optimization

Summary

This chapter describes the invasive weed optimization (IWO) algorithm, which mimics weed’s adaptive patterns. This chapter contains a literature review of the IWO, an overview of weeds’ biology, a description of the mapping of the IWO algorithm to weeds’ biology, a thorough explanation of the steps of the IWO algorithm, and a pseudocode of the IWO algorithm.

13.1 Introduction

Invasive weed optimization (IWO) was developed by Mehrabian and Lucas (2006). They solved two engineering problems and compared the results with other algorithms including the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, the shuffled frog leading algorithm (SFLA), and the simulated annealing (SA) algorithm. The results showed a relatively superior performance by the IWO. The IWO has been implemented in a variety of engineering optimization problems. Mehrabian and Yousefi‐Koma (2007) applied the IWO to optimize the location of piezoelectric actuators on a smart fin. Mallahzadeh et al. (2008) tested the flexibility, effectiveness, and efficiency of the IWO in optimizing a linear array of antenna and compared the computed results with those of the PSO algorithm. Sahraei‐Ardakani et al. (2008) implemented IWO to optimize the generation of electricity. Roshanaei et al. (2009) applied the IWO to optimize uniform linear array (ULA) used in wireless networks, such as commercial cellular systems, and compared their results with those from the GA ...

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