Conclusion and Future Prospects
摘要
Unlike their deterministic counterparts, metaheuristic optimization algorithms are practically proficient yet disreputably hard to analyze and understand. The stochastic nature lies in the metaheuristic properties that generally search for the optimized solution based on the trial-and-error attempt.