Virulence Optimization Algorithm

doi:10.1016/j.asoc.2016.02.038

Virulence Optimization Algorithm

2016-06-01

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AI

DOI：10.1016/j.asoc.2016.02.038

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In this paper, a new optimization algorithm to solve continuous and non-linear optimization problems is 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 body cells, reproduction (cloning) of these cells to prompt invasion operation of ready-to-infect regions and then escaping from infected regions (to avoid immune reaction) is the basis of this evolutionary optimization algorithm. Like many evolutionary algorithms, the Virulence Optimization Algorithm (VOA) starts the optimization process with an initial population consisting of viruses and host cells. The host cell population represents the resources available in host environment or the region containing the global optimum solution. The virus population infiltrates the host environment and attempts to infect it. In the optimization procedure, at first the viruses reside in the constituted regions or clusters of the environment called virus groups (via K-means clustering). Then they scatter in host environment through mutation (Drifting) and recombination (Shifting) operators. Then among the virus groups, the group with highest mean fitness is chosen as escape destination. Before the escape operation commences, the best viruses in each virus group are recognized and undergoes a cloning operation to spread the Virulence in the host environment. This procedure continues until the majority of the virus population is gathered in the region containing the maximum resources or the global optimum solution. The novelty of the proposed 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 best viruses for rapid and excessive infection of the host environment and (3) the mechanism of escaping from the infected region. Simulating the first mechanism in the virus life enables the proposed algorithm to generate new and fittest virus varieties. The cloning mechanism facilitates the excessive spread of the fittest viruses in the host environment to infect the host environment more quickly. Also, to avoid the immune response, the fittest viruses (with a great chance of survival) are duplicated through the cloning process, and scattered according to the Vicinity Region Radius of each region. Then, the fittest viruses escape the infected region to reside in a region which possess the resources necessary to survive (global optimum). The evaluation of this algorithm on 11 benchmark test functions has proven its capability to deal with complex and difficult optimization problems. (C) 2016 Elsevier B.V. All rights reserved.

Keyword:

Optimization

Virulence

Virus cloning

Host environment

Continuous and non-linear functions

Virulence Optimization Algorithm

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