Pareto dominance based Multiobjective Cohort Intelligence algorithm.

• Multiple features are strategically implemented in MOCI to enhance its capability. • Dual stopping criteria implemented to assess performance of all the algorithms in stringent manner. • Performance is assessed over 8 algorithms, 5 benchmark test suite, 7 real problems with 3 metrics. • Framework for exploratory analysis of performance metrics is proposed. • MOCI performance is statistically validated and verified. 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. [ABSTRACT FROM AUTHOR]