Based on Continuous Tabu Search with Sine map and Staged strategy (CTS-SS) algorithm, an improved CTS-SS (iCTS-SS) algorithm is proposed to achieve rapid convergence and enhance the robustness of the CTS-SS algorithm with strategies of adaptive neighborhood radius and awarding search iterations. Unlike a fixed neighborhood radius used in the CTS-SS algorithm, the iCTS-SS algorithm utilizes the adaptive neighborhood radius to improve the search capabilities. According to whether the optimal result is replaced, it could be narrowed for centralized search or expanded for decentralized search. By this way, the result can be better than using regular iterations. The experiments adopt various typical functions as numerical experiment cases to evaluate the robustness, accuracy and efficiency of iCTS-SS. And for comparison, these functions are also solved by CTS-SS and Modified Staged Continuous Tabu Search (MSCTS), which is one of the latest continuous tabu search algorithms. Experimental results show that iCTS-SS outperforms both CTS-SS and MSCTS in statistical significance.

中文翻译：

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一种改进的具有自适应邻域半径和增加搜索迭代次数策略的连续禁忌搜索算法

基于正弦图连续禁忌搜索和分阶段策略（CTS-SS）算法，提出了一种改进的CTS-SS（iCTS-SS）算法，以实现快速收敛，增强自适应邻域策略的CTS-SS算法的鲁棒性。半径和奖励搜索迭代。与 CTS-SS 算法中使用的固定邻域半径不同，iCTS-SS 算法利用自适应邻域半径来提高搜索能力。根据是否替换最优结果，可以缩小集中搜索，也可以扩大分散搜索。通过这种方式，结果可以比使用常规迭代更好。实验采用各种典型函数作为数值实验案例来评估iCTS-SS的鲁棒性、准确性和效率。为了比较，这些功能也通过 CTS-SS 和改进的分阶段连续禁忌搜索 (MSCTS) 解决，这是最新的连续禁忌搜索算法之一。实验结果表明，iCTS-SS 在统计显着性上优于 CTS-SS 和 MSCTS。