Particularly interesting group consists of algorithms that implement co-evolution or co-operation in natural environments, giving much more powerful implementations. The main aim is to obtain the algorithm which operation is not influenced by the environment. An unusual look at optimization algorithms made it possible to develop a new algorithm and its metaphors define for two groups of algorithms. These studies concern the particle swarm optimization algorithm as a model of predator and prey. New properties of the algorithm resulting from the co-operation mechanism that determines the operation of algorithm and significantly reduces environmental influence have been shown. Definitions of functions of behaviour scenarios give new feature of the algorithm. This feature allows self controlling the optimization process. This approach can be successfully used in computer games. Properties of the new algorithm make it worth of interest, practical application and further research on its development. This study can be also an inspiration to search other solutions that implementing co-operation or co-evolution.
A group of immune systems is similar to a multipopulation system. Immune systems can be influenced by vaccines and serums, similarly to that which occurs in nature. The discussed algorithm has more parameters of work control than other immune algorithms. Fractal and multi-fractal analyses of the proposed algorithm, supported by quantitative analysis, are discussed. Fractal and multifractal analyses illustrate the algorithm behaviour. These analyses allow comparing algorithm settings considering their impact on the exploration and exploitation of the solution space. Fractal and multifractal analyses will be a valuable completion of knowledge of their work mechanisms.
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