中文摘要：

在综合考虑了环境对生物进化的影响、免疫算法的结构以及遗传算法部分算子的基础上，提出一种考虑环境作用的协同免疫遗传算法（ESIGA），以实现提高算法搜索速度和全局搜索能力的目标．在该算法中，设计了克隆环境演化算子和自适应探索算子，并构造了3个子种群协同进化以发挥克隆环境演化算子的影响，从而提高算法的全局搜索能力．引入的自适应探索算子和克隆环境演化算子，使算法具备了一定的学习能力，可加速搜索和防止早熟．构建的主种群和协同种群相互影响，使得算法对环境具有改良能力，加强了克隆环境演化算子的性能，而精英种群则加强了算法在优质个体邻域的搜索能力．采用13个常用无约束优化问题测试函数对算法做了检验，测试数据表明：ESIGA算法与正交遗传算法相比，其搜索速度要快于正交遗传算法1～2倍，并能够处理1000维的高维优化问题．

英文摘要：

The function of environment effecting creature evolution, the process of immune clone algorithm and some operators of genetic algorithm are considered synthetically. An environmentbased synergic immune genetic algorithm （ESIGA） is presented to improve the searching rate and global searching ability of optimal algorithm. The clone-environment operator and self adaptive search operator are designed to make the algorithm possess learning capability to accelerate the searching rate and avoid prematurity. And three groups of sub-population are used for improving the global searching ability, where the interaction between main sub-population and synergic subpopulation improves the environment and exerts clone-environment operator effect, and the elitist sub-population enhances the neighborhood searching. Then 13 classical benchmarks of non-constraint optimal problem are executed by the proposed algorithm. The test results demonstrate that ESIGA searching rate is twice as QGA/Q. ESIGA is suit to deal with 1 000-dimension non-constraint optimal problems.