In the recent days, several novel and specialized algorithms are coming up for solving particular class of problems. However, their performance on new benchmark or real-world problem remains unsure. This paper proposes a novel Multiobjective Cohort Intelligence (MOCI) algorithm. It is based on Pareto dominance and coevolutionary design principles to achieve efficient, effective, productive and robust performance. The capability of MOCI algorithm is enhanced through use of multiple features for balance of exploration versus exploitation, search towards promising region and avoidance of search stagnation. The performance of MOCI is assessed against the state-of-the-art algorithms, such as: ARMOEA, CMOPSO, hpaEA, LMOCSO, LSMOF, NMPSO and WOFSMPSO across multiple test suites including Classical, ZDT, DTLZ, WFG and UF. The performance assessment is conducted with truly uncorrelated performance metrics. In this regard, an exploratory approach of multiple correlation analysis is proposed. Performance of MOCI algorithm is statistically verified and validated using PROMETHEE-II and nonparametric statistical tests. MOCI is capable of achieving well converged and diversified solutions on most of the test as well as real world problems. The success of MOCI is attributed to multiple features incorporated in the algorithm. In the future, MOCI could be applied to challenging problems in engineering and management.

近年来，出现了一些新颖且专门的算法来解决特定类别的问题。但是，它们在新基准或实际问题上的性能仍不确定。本文提出了一种新颖的多目标队列智能（MOCI）算法。它基于帕累托优势和协同进化设计原则，以实现高效，有效，富有成效和强大的性能。通过使用多种功能来平衡勘探与开发，向有希望的地区进行搜索以及避免搜索停滞，MOCI算法的功能得到了增强。MOCI的性能是根据最新的算法进行评估的，这些算法包括：跨多个测试套件（包括Classical，ZDT，DTLZ，WFG和UF）的ARMOEA，CMOPSO，hpaEA，LMOCSO，LSMOF，NMPSO和WOFSMPSO。使用真正不相关的绩效指标进行绩效评估。在这方面，提出了一种探索性的多重相关分析方法。使用PROMETHEE-II和非参数统计检验对MOCI算法的性能进行统计验证。MOCI能够针对大多数测试以及现实世界中的问题实现融合良好且多样化的解决方案。MOCI的成功归因于算法中包含的多个功能。将来，MOCI可以应用于工程和管理中的难题。MOCI能够针对大多数测试以及现实世界中的问题实现融合良好且多样化的解决方案。MOCI的成功归因于算法中包含的多个功能。将来，MOCI可以应用于工程和管理中的难题。MOCI能够针对大多数测试以及现实世界中的问题实现融合良好且多样化的解决方案。MOCI的成功归因于算法中包含的多个功能。将来，MOCI可以应用于工程和管理中的难题。