MATLAB Implementation of Invasive Weed Optimization Algorithm

Invasive Weed Optimization (IWO) algorithm is a swarm intelligence technique inspired by the colonization behavior of weeds in natural ecosystems. This algorithm simulates the process of weed propagation, competition, and elimination in ecological systems to solve optimization problems, particularly effective for nonlinear complex optimization scenarios. The core algorithm logic consists of three key phases implemented through MATLAB functions: Population Initialization - Randomly generates initial weed individuals in the search space using MATLAB's rand() function, where each individual represents a potential solution Spatial Dispersion - Simulates weed seed dispersal mechanism where fitter individuals produce more offspring, with seed quantity dynamically adjusted based on fitness evaluation using a nonlinear mapping function Competitive Exclusion - When population exceeds the maximum limit, the algorithm eliminates inferior individuals by sorting fitness values and maintaining population stability through a truncation operation MATLAB implementation typically includes these components: fitness function evaluation module, seed dispersal probability model, nonlinear mutation operator, and population size control logic. The algorithm converges to optimal solutions iteratively through a while/for loop structure, with its adaptive dispersion mechanism effectively balancing global exploration and local exploitation capabilities. Compared to traditional genetic algorithms, IWO offers distinct advantages: no crossover operation required, simpler parameter configuration using minimal control variables, faster convergence speed demonstrated through reduced iterations, and particular suitability for high-dimensional optimization problems. Key MATLAB functions involved include fitness evaluation, population sorting, and dynamic parameter adjustment. Typical applications include neural network training using trainlm optimization, engineering parameter optimization, and machine learning hyperparameter tuning scenarios.