"MOSS" multiobjective scatter search applied to non-linear multiple criteria optimization
"MOSS" multiobjective scatter search applied to non-linear multiple criteria optimization
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DOI：
10.1016/j.ejor.2004.08.008
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发表时间：
2006-03
期刊：
影响因子：
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通讯作者：
Ricardo P. Beausoleil
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文献类型：
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作者：
Ricardo P. Beausoleil
针对高维有界约束非线性连续向量优化问题，提出了一种多准则分散搜索算法，采用多起点禁忌搜索（TS）作为多样化生成方法，每个TS具有自己的起点、新近记忆和期望阈值。频率存储器用于使搜索多样化，并且在TS之间共享。应用帕累托关系以指定最佳生成解的子集作为参考解。选择函数称为克雷默选择函数用于将参考解分成两个子集。欧几里德距离被用作相异性的度量，以便找到要组合的不同解决方案。将参比溶液的线性组合用作溶液组合方法。决策空间和目标空间中的“球”用于避免重复。在分散阶段采用了不同禁忌期的不同禁忌集，以提高搜索的多样性。我们的方法的性能进行比较与帕累托最优的前沿和其他三个国家的最先进的MOEA从文献中的一套测试问题。
This paper introduces a multiple criteria scatter search to deal with bounded constrained non-linear continuous vector optimization problems of high dimension, applying a MultiStart Tabu Search (TS) as a diversification generation method, each TS works with its own starting point, recency memory, and aspiration threshold. Frequency memory is used to diversify the search and it is shared between the TS. A Pareto relation is applied in order to designate a subset of the best generated solutions to be reference solutions. A choice function called Kramer Choice function is used to divide the reference solutions in two subsets. The Euclidean distance is used as a measure of dissimilarity in order to find diverse solutions to be combined. Linear combinations of the reference solutions are used as a solution combination method. “Balls” in the decision space and the objective space are used to avoid duplications. Different tabu sets with different tabu tenures are employed in the scatter phase to enhance the diversity of the search. The performance of our approach is compared with Pareto-optimal frontiers and three other state-of-the-art MOEAs for a suite test problems taken from the literature.