A cohort refers to a group of candidates interacting and competing with one another. The basic idea of cohort is inspired from the social tendency of following/learning from one another and adapting the qualities of certain candidate. Based upon this approach, seven variations of cohort intelligence (CI) are presented in this paper. The seven variations of CI are: follow best, follow better, follow worst, follow itself, follow median, follow roulette wheel selection and alienate-and-random selection. The proposed variations are tested on seven multimodal and three uni-modal unconstrained test functions, and the numerical results are analyzed to decide which variation works best for a particular type of problem. The performance of these variations is compared with some well-known algorithms namely PSO, CMAES, ABC, JDE, CLPSO, SADE and BSA. The analysis of variations gives very important insight about the strategy that should be followed while working in a cohort. The variations proposed may provide insight into variegated applicability domain of the CI methodology. The choice of the right variation may also further open doors for CI to solve different real-world problems.
KeywordsCohort intelligence Self-supervised learning Socio-inspired optimization Unconstrained test problems
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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