We propose a novel co-evolutionary algorithm for interactive multiple objective optimization, named CIEMO/D. It aims at finding a region in the Pareto front that is highly relevant to the Decision Maker (DM). For this reason, CIEMO/D asks the DM, at regular intervals, to compare pairs of solutions from the current population and uses such preference information to bias the evolutionary search. Unlike the existing interactive evolutionary algorithms dealing with just a single population, CIEMO/D co-evolves a pool of subpopulations in a steady-state decomposition-based evolutionary framework. The evolution of each subpopulation is driven by the use of a different preference model. In this way, the algorithm explores various regions in the objective space, thus increasing the chances of finding DM’s most preferred solution. To improve the pace of the evolutionary search, CIEMO/D allows for the migration of solutions between different subpopulations. It also dynamically alters the subpopulations’ size based on compatibility between the incorporated preference models and the decision examples supplied by the DM. The extensive experimental evaluation reveals that CIEMO/D can successfully adjust to different DM’s decision policies. We also compare CIEMO/D with selected state-of-the-art interactive evolutionary hybrids that make use of the DM’s pairwise comparisons, demonstrating its high competitiveness.

我们提出了一种用于交互式多目标优化的新型协同进化算法，称为CIEMO / D。其目的是在帕累托前沿找到与决策者（DM）高度相关的区域。因此，CIEMO / D要求DM定期比较当前种群的解决方案对，并使用这种偏好信息来偏向进化搜索。与现有的仅涉及单个种群的交互式进化算法不同，CIEMO / D在基于稳态分解的进化框架中共同演化了一组亚种群。每个亚群的进化是通过使用不同的偏好模型来驱动的。通过这种方式，该算法探索了目标空间中的各个区域，从而增加了找到DM最优选解决方案的机会。为了提高进化搜索的速度，CIEMO / D允许解决方案在不同亚人群之间的迁移。它还基于合并的偏好模型和DM提供的决策示例之间的兼容性，动态更改子群体的大小。广泛的实验评估表明，CIEMO / D可以成功适应不同DM的决策策略。我们还将CIEMO / D与选定的最先进的交互式进化混合动力进行了比较，这些混合动力利用了DM的成对比较，证明了其高竞争力。广泛的实验评估表明，CIEMO / D可以成功适应不同DM的决策策略。我们还将CIEMO / D与选定的最先进的交互式进化混合动力进行了比较，这些混合动力利用了DM的成对比较，证明了其高竞争力。广泛的实验评估表明，CIEMO / D可以成功适应不同DM的决策策略。我们还将CIEMO / D与选定的，最先进的交互式进化混合动力系统进行了比较，这些混合动力系统利用了DM的成对比较，证明了其高竞争力。