Motorbike Courier Optimization: A Novel Parameter-Free Metaheuristic for Solving Constrained Real-World Optimization Problems

Abstract

This paper introduces the Motorbike Courier Optimization (MCO) algorithm, a novel human-based metaheuristic designed to solve complex constrained optimization problems, particularly those derived from real-world applications. The key innovation lies in simulating the adaptive and strategic behavior of motorbike couriers who must efficiently navigate urban environments under pressure, balancing speed, safety, route optimization, and real-time decision-making. Inspired by these real-life behaviors, MCO incorporates three algorithmic phases—initialization, exploration, and exploitation—mapped mathematically to dynamic motion, flexible path selection, and convergence strategies. A distinct advantage of MCO is its parameter-free design, eliminating the need for algorithm-specific tuning. To validate its optimization capability, MCO is tested on the CEC 2011 benchmark suite, which includes 22 constrained real-world optimization problems in engineering and industrial contexts. These benchmarks cover diverse challenges such as trajectory design, energy systems, and chemical processes, providing a comprehensive evaluation framework. The performance of MCO is assessed using six statistical indicators: mean, best, worst, standard deviation, median, and rank. Comparative simulations are conducted against nine state-of-the-art metaheuristics: SFOA, CFOA, COA, POA, OOA, MPA, RSA, AVOA, and WSO. Experimental results confirm that MCO demonstrates superior performance across the majority of benchmark problems. This dominance is attributed to its unique behavioral modeling, robust adaptability, and efficient balance between global exploration and local exploitation. The results position MCO as a competitive and innovative optimization tool with high potential for solving a wide range of constrained real-world problems.