Opposition and Chaos-Based Water Cycle Algorithm on Engineering Benchmark Problems

Chaos-based learning is a popular method that combines with optimized techniques to achieve the best fitness values by preventing early convergence and improving initial conditions. Chaos enhances the search process, while opposition learning helps improve the population development matrix, ultimately boosting result quality. This paper introduces two new concepts in machine learning. It proposes an improved Opposition and Chaos-based Water Cycle Algorithm (OCWCA) to effectively achieve the best fitness value through cost calculation and to validate the results of objective functions on 15 engineering benchmark problems with constraints. The Water Cycle Algorithm (WCA), a hydrology-based method, provides global search locations based on the flow of streams and rivers toward the sea using predefined control parameters to generate a population matrix. A convergence plot for OCWCA compared to WCA demonstrates significant improvement in results, incorporating learnings from the WCA method and yielding the best fitness data.