关键词: 高维背包问题, 克隆选择算法（CSA）, 受体编辑机制, 修补策略

Abstract: Since clonal selection algorithm (CSA) on high-dimensional knapsack problems (KPs) can only obtain a low feasible rate, and falls easily into local search, this paper proposes an improved clonal selection algorithm (CSA-ER) to solve high-dimensional KPs. In CSA-ER, a receptor editing mechanism is developed based on antibodies diversity function in immune system. Also, a repeat repair strategy is introduced to enhance the ability of handling constraints. CSA-ER is compared with several variants of CSA (CSA-M, CSA-E, CSA-MR) and two other intelligent algorithms on KPs in simulation experiments. The results show that CSA-ER has strong exploitation and convergence capability. Meanwhile, the sensitivities of three parameters (selection rate α, editing rate Tr, and basic gene segment length σ) in CSA-ER are also analyzed, and the appropriate parameter settings are obtained in the last.

Key words: high-dimensional knapsack problem, clonal selection algorithm (CSA), receptor editing mechanism, repair strategy