Immune Clonal MO Algorithm for 0/1 Knapsack Problems
Immune Clonal MO Algorithm for 0/1 Knapsack Problems
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DOI：
10.1007/11881070_115
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发表时间：
2006-09
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影响因子：
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通讯作者：
Ronghua Shang;Wenping Ma;Wei Zhang-
中科院分区：
文献类型：
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作者：
Ronghua Shang;Wenping Ma;Wei Zhang-
提出了一种基于免疫克隆原理的多目标优化算法来求解0/1背包问题。这种算法被称为免疫克隆MO算法（ICMOA）。在ICMOA中，抗体群体被分为非优势抗体群体和优势抗体群体。同时，允许非优势抗体存活和克隆。采用两集覆盖度量来解决问题。该定量度量用于测试向帕累托最优前沿的收敛。对0/1背包问题的仿真结果表明，与SPEA、NSGA、NPGA和VEGA相比，ICMOA在大多数问题上都能找到更好的解的传播和更好的收敛性。
In this paper, we introduce a new multiobjective optimization (MO) algorithm to solve 0/1 knapsack problems using the immune clonal principle. This algorithm is termed Immune Clonal MO Algorithm (ICMOA). In ICMOA, the antibody population is split into the population of the nondominated antibodies and that of the dominated antibodied. Meanwhile, the nondominated antibodies are allowed to survive and to clone. A metric of Coverage of Two Sets is adopted for the problems. This quantitative metric is used for testing the convergence to the Pareto-optimal front. Simulation results on the 0/1 knapsack problems show that ICMOA, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared with SPEA, NSGA, NPGA and VEGA.