Immunity-based hybrid evolutionary algorithm for multi-objective optimization

The development of evolutionary algorithms for optimization has been a growing research area. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrainedmulti-objectiveoptimization problemsis developed. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain Pareto optimal in an efficient manner. The algorithm was verified with nine benchmarking functions on its global optimal search ability and compared with four optimization algorithms to assess its diversity and spread. It is found that the immunity-based algorithm provides a useful means for solving optimization problems, and has proved its capability in global optimal search in multi-objective optimization.

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