V-MFO: Variable Flight Mosquito Flying Optimization

Real-world optimization problems in engineering are becoming increasingly complex and require more efficient techniques for their solution. This paper presents a new optimization algorithm, namely variable flight mosquito flying optimization (V-MFO). It mimics the behavior of mosquitoes to find a hole or an irregularity in a mosquito net. It incorporates a variable flying constant and precision movements of the proboscis instead of constant flying and sliding motion of the mosquitoes likewise in simple mosquito flying optimization (MFO). The algorithm was examined for the global minima on diverse types of benchmark functions of diverse dimensions and modality, such as Ackley, Griewank, Rastrigin, Rosenbrock, and Schwefel functions of 5, 10, and 30 dimensions. The results were compared with five established methods, namely genetic algorithm (GA), particle swarm optimization (PSO), seven-spot ladybird optimization (SLO), artificial bees’ colony (ABC), and mosquito flying optimization (MFO). Consequently, this algorithm was found to be efficient, convergent, and accurate.