A neighbourhood-clonal selection learning algorithm(N-Clonalg)is proposed in this paper

which is combined with the idea of biological immune clonal selection system and multi-agent technology. Different from other artificial immune algorithms

N-Clonalg is based on the grid environment

and contains three main search operators

i.e.

N-clonal selection

N-competition and self-learning operators.Combining global and local searching operations

N-Clonalg overcomes the phenomena of precocious and slow convergence

and can better achieve the global optimal solutions effectively in the individual space

which is proved in multi-modal benchmarks.Distillation resources optimization shows that it has better search performance.