关键词: 免疫算法 正交交叉 单形交叉 多极变异 函数优化

Abstract:

Considering the drawbacks of easily being trapped in a local optimal solution and low convergence velocity of the clone selection algorithms in solving high dimmensional function optimization, this paper proposes a new immune evolutionary algorithm based on the interior learning mechanism of biologica  l immune systems and evolutionary algorithms. The new algorithm includes orthogonal crossover, simplex crossover, clone, multipolar mutation and selecti on. The idea of evolutionary computation is integrated into clone selection, and a new mutation operator is proposed. This new algorithm can guarantee t    he diversity of the population and improve the global search ability. Theoretical analyses prove that NIEA converges to the global optimum. Different fu     nctions are utilized to test this method and the simulation results suggest that this algorithm has good performance.

Key words: immune algorithm, orthogonal crossover, simplex crossover, multipolar mutation, function optimization