Negotiators Algorithm: A Novel Parameter-Free Metaheuristic Inspired by Human Negotiation Behaviour
International journal of intelligent engineering and systems · 2025
This paper introduces a novel metaheuristic optimization algorithm named the Negotiators Algorithm (NA), inspired by the human negotiation process.The algorithm simulates negotiation dynamics among candidate solutions, incorporating key behaviors such as conflict, mutual influence, concession, and consensus-building to guide the search process effectively toward global optima.To evaluate the performance of NA, a comprehensive simulation study was conducted using 23 well-established benchmark functions, including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal test problems.The proposed algorithm was rigorously compared with nine recent and competitive metaheuristic algorithms.The experimental results demonstrate that NA consistently outperforms competing algorithms in terms of convergence speed, accuracy, and robustness, particularly on complex multimodal and high-dimensional landscapes.The boxplot analyses further confirm NA's superior stability and reduced variance across trials.Due to its simplicity, adaptability, and parameter-free nature, NA represents a promising approach for tackling continuous optimization problems.This work not only proposes a novel optimization framework but also opens new directions for designing intelligent algorithms based on social and behavioral metaphors.Future research may explore theoretical convergence analysis, hybridization, and real-world applications in engineering and machine learning.