Mosquito Optimizer: A Nature-inspired Meta-heuristic Algorithm

Mosquitoes employ a sophisticated combination of sensory cues to locate and feed on hosts. This paper elucidates the sequential steps involved in mosquito hunting behavior, encompassing host cue detection, attraction to hosts, utilization of visual cues, landing and feeding mechanisms, and sensing of danger. The hunting behavior may vary among species and is influenced by environmental factors. To optimize this behavioral model, a Mosquito Optimizer Algorithm (MOA) is proposed. The algorithm entails the representation of candidate solutions akin to mosquito sensory cues, initialization of solution populations, exploration of the search space through fitness evaluation, exploitation of promising solutions, and adaptation to threats. Termination criteria are defined to conclude the optimization process. Mathematically, the optimization process involves defining an objective function to be optimized, initializing solution populations, evaluating fitness, updating the global best position, adjusting direction towards attractive targets, updating velocities, and iteratively updating positions based on velocities until termination criteria are met. The proposed algorithm demonstrates efficacy in solving optimization problems, drawing inspiration from mosquito hunting behavior and offering potential applications across various domains. The source code is available at: https://github.com/Alqushaibi/MOA.