Function optimization algorithm based on SIRQV epidemic dynamic model
A new function optimization algorithm SIRQVA is created based on the SIRQV epidemic model. The algorithm supposes that some animal individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease exists in the ecosystem and infects among individuals, the disease attacks a part of features of an individual. SIRQVA possesses of 5 states (S, I, R, Q and V) and 13 state transitions, each state transition is equivalent to an operator. SIRQVA uses the 13 state transitions to make information exchange from many different ways, the information exchange among individuals are comprehensive and abundant, not only can it make individuals move to global optima quickly, but also it reduces the probability individuals drop into local optima, and it can expand the scope of search. When individuals staying at different states make information exchange, only a small number of features join in computation. Because features selected for processing decreases greatly, when solving complicated optimization problems, especially some high dimensional optimization problems, it can greatly enhance the convergence speed. SIRQVA uses a great number of individuals to make initialization, a small number of work individuals to make search. The method is very effective for dealing with some optimization problems with high condition number, because increasing the number of initialization individuals greatly within the area next to global optima can enhance substantially the probability of locating global optima accurately. A small number of work individuals to make search for global optima, the search speed can increase greatly. SIRQVA enhances its search performance not by good performance of a single operator, but by cooperation among numerous operators.
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