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Bonobo optimizer (BO) is a recent metaheuristic algorithm inspired by the social behavior of bonobos. BO maintains a robust search strategy based on two distinct phases that involve interesting mechanisms, such as the fission–fusion method for selection and an efficient process for producing new candidate solutions. Despite its remarkable capacity, BO presents a critical flaw that corresponds to an inappropriate balance between exploration and exploitation. Under such conditions, suboptimal or even poor solutions can be obtained. This paper proposes a modified BO algorithm in which the trajectory of each search agent is modified dynamically through the adaptation of the main parameters of the algorithm. With this new mechanism, the algorithm allows for the exploration and exploitation of different regions of the solution space and determines the global optimum in a faster manner. The performance of the proposed algorithm is evaluated by considering two scenarios. One is the optimization of 23 benchmark functions and the second is the problem of power allocation in wireless networks. The results show that the proposed scheme enhances the performance of the basic BO method by increasing the robustness and providing a better solution quality. [ABSTRACT FROM AUTHOR]

Differential privacy (DP), as a promising privacy-preserving model, has attracted great interest from researchers in recent years. At present, research on the combination of deep learning and DP is active. In contrast, another widely used artificial intelligence technique, swarm intelligence (SI), has received little attention in the context of DP even though it also triggers privacy concerns. For this reason, this paper attempts to combine DP and SI for the first time and proposes a general differentially private swarm intelligence algorithm framework (DPSIAF). By utilizing the exponential mechanism, this framework can easily develop existing SI algorithms into the private versions. As examples, we apply it to four popular SI algorithms, and the related analyses demonstrate the effectiveness of our DPSIAF. More importantly, our private algorithms also exhibit some interesting experimental phenomena. In some cases, their performance is not strictly affected by the privacy budget, and one of them even outperforms its non-private version. These findings are different from the conventional cognition, which indicates DP owns the potential to serve as an optimization tool for some SI algorithms. Our study may provide a new perspective on DP and promote the synergy between metaheuristic optimization community and privacy computing community. [ABSTRACT FROM AUTHOR]