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چکیده
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tIn this paper, a new optimization algorithm to solve continuous and non-linear optimization problemsis introduced. This algorithm is inspired by the optimal mechanism of viruses when infecting body cells.Special mechanism and function of viruses which includes the recognition of fittest viruses to infect bodycells, reproduction (cloning) of these cells to prompt “invasion” operation of ready-to-infect regions andthen escaping from infected regions (to avoid immune reaction) is the basis of this evolutionary optimiza-tion algorithm. Like many evolutionary algorithms, the Virulence Optimization Algorithm (VOA) startsthe optimization process with an initial population consisting of viruses and host cells. The host cellpopulation represents the resources available in host environment or the region containing the globaloptimum solution. The virus population infiltrates the host environment and attempts to infect it. Inthe optimization procedure, at first the viruses reside in the constituted regions or clusters of the envi-ronment called virus groups (via K-means clustering). Then they scatter in host environment throughmutation (Drifting) and recombination (Shifting) operators. Then among the virus groups, the group withhighest mean fitness is chosen as escape destination. Before the escape operation commences, the bestviruses in each virus group are recognized and undergoes a cloning operation to spread the Virulence inthe host environment. This procedure continues until the majority of the virus population is gatheredin the region containing the maximum resources or the global optimum solution. The novelty of theproposed algorithm is achieved by simulating three important and major mechanisms in the virus life,namely (1) the reproduction and mutation mechanism, (2) the cloning mechanism to generate the bestviruses for rapid and excessive infection of the host environment and (3) the mechanism of escapingfrom the infected region. Simulating the first mechanism in the
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