Optimization Using Ant Colony Algorithm in Continuous Domain Space

In this article, we explore the application of ant colony algorithms for solving optimization problems in continuous domain spaces. We introduce a grid-based partitioning strategy that effectively handles unconstrained nonlinear optimization problems. The algorithm implementation involves discretizing the continuous search space into grid cells, where artificial ants deposit pheromones to guide the search toward promising regions. We provide practical code examples demonstrating key functions such as probability calculation for path selection and pheromone update mechanisms. These examples illustrate the method's effectiveness and practical applicability through benchmark optimization functions. Additionally, we discuss potential enhancements including adaptive grid refinement and hybrid approaches combining local search techniques. Readers will gain insights into implementing continuous-domain ant colony optimization and applying it to real-world engineering problems, with code snippets highlighting critical components like solution initialization and fitness evaluation.