Exploring the Potential of Invasive Weed Optimization: A Population-Based Metaheuristic for Optimization Problems

Abstract

Invasive Weed Optimization (IWO) is a population-based metaheuristic algorithm inspired by the invasive behaviour of weeds. It aims to solve optimization problems through a process of competition, reproduction, mutation, and selection. Introduced by Mehrabian and Lucas in 2006, IWO has since undergone several iterations and improvements. The algorithm begins by initializing a population of candidate solutions, represented as "weeds." Each weed is evaluated based on a fitness function that captures the optimization criteria of the problem. Through reproduction, fitter individuals are selected as parents, and new offspring are generated through recombination and crossover operations. Mutation introduces random changes to the offspring, allowing for exploration of the search space. Competition plays a crucial role in IWO. Offspring compete with existing weeds, and if an offspring exhibits superior fitness, it replaces a less fit weed in the population. This competitive mechanism ensures that stronger solutions survive and propagate, gradually improving the quality of solutions over iterations. Termination criteria determine when the algorithm stops iterating. Common criteria include reaching a maximum number of iterations, achieving convergence, or surpassing a solution quality threshold. Once the termination condition is met, the best solutions found during the optimization process are returned as the results.IWO has shown effectiveness in various problem domains, including engineering design, data mining, image processing, and finance. It balances exploration and exploitation, allowing for efficient search of the solution space. However, IWO also has limitations, such as sensitivity to parameter settings and lack of global convergence guarantee. Despite these limitations, IWO has been extensively studied and improved over time. Its adaptability, population-based approach, and competition mechanism make it a valuable tool for solving optimization problems, offering an alternative to other well-established algorithms like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and Simulated Annealing.