Abstract
A novel multiobjective optimization immune algorithm in dynamic environments, as associated with Pareto optimality and immune metaphors of germinal center in the immune system, is proposed to deal with a class of dynamic multiobjective optimization problems which the dimension of the objective space may change over time. Several immune operators, depending on both somatic maturation and T-cell regulation, are designed to adapt to the changing environment so that the algorithm can achieve a reasonable tradeoff between convergence and diversity of population, among which an environmental recognition rule related to the past environmental information is established to identify an appearing environment. Preliminary experiments show that the proposed algorithm cannot only obtain great superiority over two popular algorithms, but also continually track the time-varying environment. Comparative analysis and practical application illustrate its potential.
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