A Meta-Heuristic Algorithm Based on the Happiness Model

Recent work has attempted to determine the appropriate global minimum for complex problems. The paper presents a population and direct-based swarm optimization algorithm called the happiness optimizer (HPO) algorithm. An HPO algorithm is designed based on personal behavior and demonstrated in 30 and 100 dimensions on benchmark functions. The model includes four questions: “what do you want?”, “what do you have?”, “what do others have?”, and “what happened?”, which guide the development of a happiness behavior model. By considering the balancing between exploration and exploitation operators in the search space problem, efficiency, robustness, and stability were demonstrated for synthetic and real cases. For comparison, our proposed algorithm and some well-known algorithms will be 30 times applied on the benchmark functions and then compared with statistical value and Wilcoxon signed-rank test. As a consequence, the performance, reliability, and stability of our work have been demonstrated better than the others. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.