Abstract: Farmland Fertility Algorithm (FFA) is a recent nature-inspired metaheuristic algorithm for solving optimization problems. Nevertheless, FFA has some drawbacks: slow convergence and imbalance of diversification (exploration) and intensification (exploitation). An adaptive mechanism in every algorithm can achieve a proper balance between exploration and exploitation. The literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these drawbacks. Therefore, in this paper, twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising solutions. Furthermore, the Quasi-Oppositional-Based Learning (QOBL) mechanism enhances the exploration speed and convergence rate; we name a CQFFA algorithm. The improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification component. The results obtained show that the proposed CQFFA model has been significantly improved. It is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and visually. Also, the CQFFA algorithm has evaluated six real-world engineering problems. The experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.

Key words: Nature-inspired algorithm , · Farmland fertility algorithm , · Chaotic maps , · Quasi , · Engineering optimization problems