Free-space optical (FSO) wireless sensor network is rapidly growing for underwater communication applications. However, the high-energy loss and propagation distance are the key concerns during data transmission in SDN-enabled underwater wireless sensor networks (UWSNs). In addition, long-distance free-space data transmission in UWSNs relies heavily on FSO communication. Thus, FSO communication is integrated with SDN-enabled UWSNs to maximizing the network lifespan called SDN-enabled free-space optical underwater wireless sensor networks (FSO-UWSNs). Furthermore, clustering and routing can effectively balance the network load for energy-efficient data delivery in SDN-enabled FSO-UWSNs. However, choosing the optimal control nodes (CNs) in clustering is considered as an NP-hard problem. Accordingly, self-adaptive genetic approach-based particle swarm optimization (SAGA-PSO) is proposed as a cluster-based routing to optimize the CNs in heterogeneous SDN-enabled FSO-UWSNs. The proposed hybrid model of metaheuristics and genetic mutation, in which the native PSO is amended with the self-adaptive inertia weights and genetic mutation operation to identify the CNs based on genetic diversity dynamically. In addition, a novel fitness function is proposed to balance the cluster size by considering the most significant parameters like energy and distance of network devices. The SAGA-PSO is simulated using the ns-3 simulator, and SDN policies are controlled via the ONOS controller. Moreover, the proposed nature-inspired SAGA-PSO approach outperforms the existing state of arts by considering the performance metrics such as; alive nodes, stability period, average residual energy, the packet transmitted to CS, average delay, and fitness value.

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