Competition-amensalism optimization: A novel metaheuristic optimization algorithm

Abstract

• A new metaheuristic, the Competition-Amensalism Optimization (CAO), is introduced based on ecological interaction theory. • CAO employs competition and amensalism operators to balance global exploration and local exploitation. • The algorithm is parameter-free, offering simple implementation and strong adaptability across problem types, which provide fast, robust convergence, avoiding premature stagnation in complex landscapes. • CAO achieves superior performance on 32 benchmark functions and seven real-world engineering problems. • CAO demonstrates excellent scalability, outperforming state-of-the-are optimizers on 30-, 100-, and 1000-dimensional problems. This study introduces a novel metaheuristic optimization algorithm, termed the Competition-Amensalism Optimization (CAO), inspired by competitive interactions in ecological systems. CAO employs a dual-phase search strategy: a competition phase to enhance global exploration and an amensalism phase to intensify local exploitation. This synergy ensures a robust balance between exploration and exploitation, which is essential for addressing complex, high-dimensional optimization problems. The performance of CAO was rigorously evaluated using 32 diverse benchmark functions and seven classical engineering design problems. Experimental results demonstrate that CAO consistently delivers high-quality solutions and exhibits superior convergence behavior compared to several state-of-the-art metaheuristic algorithms. Its adaptability enables strong performance on both unimodal and multimodal landscapes, indicating broad applicability diverse problem types. The promising results highlight CAO’s potential for real-world optimization problems. Future research will focus on adaptive interaction strategies, constrained and multi-objective optimization extensions, and large-scale validation under noisy and dynamically changing optimizations environments.