MATLAB Implementation of Niche Genetic Algorithm with Crowding Mechanism

Genetic Algorithm (GA) is an intelligent optimization algorithm that simulates biological evolution processes. In traditional genetic algorithms, insufficient population diversity often leads to premature convergence issues. The introduction of niche technology effectively maintains population diversity through specialized mechanisms. The crowding mechanism is one implementation approach for niche techniques, with its core concept being population distribution control through similarity comparisons between individuals. The algorithm calculates the distance or similarity between new individuals and existing population members. When exceeding a predetermined threshold, inferior individuals are "crowded out" of the population. This mechanism simulates the natural competition phenomenon where species compete for limited resources. In MATLAB implementation, the algorithm typically includes these key steps with corresponding code implementations: Initialization Phase: Randomly generate initial population using functions like rand() or randn(), while setting parameters such as crowding factor and crowding distance Fitness Evaluation: Compute fitness values for each individual through objective function calculations, often implemented using array operations for efficient processing Crowding Operation: Perform similarity detection for newly generated individuals using distance metrics (Euclidean, Hamming) followed by replacement operations with conditional statements Genetic Operations: Execute standard genetic operations including selection (roulette wheel, tournament), crossover (single-point, two-point), and mutation (bit-flip, Gaussian) using logical indexing and matrix manipulations Termination Condition Check: Determine algorithm termination based on preset conditions such as generation count or fitness convergence using while/for loops and break statements This algorithm is particularly suitable for multimodal function optimization problems, capable of locating multiple optimal solutions simultaneously in the solution space. By adjusting parameters like crowding distance and crowding ratio, users can control population distribution density and solution precision. MATLAB's matrix computation capabilities make it ideal for implementing such algorithms requiring extensive similarity calculations, where vectorized operations can significantly speed up distance computations between population members using built-in functions like pdist2() or custom matrix operations.