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Cloud computing (CC) systems are a form of public infrastructure that has been in use since its beginnings. In such technologies, clients can access current services according to their requirements without knowing where the service is hosted or how it is supplied, and only pay for the services that they really consume. The CC system faces several challenges. Due to the large range of clients and services offered by this platform, it can be concluded that the scheduling problem and resource usage are its primary issues. In order to successfully balance the three main qualities of service (QoS) criteria: time, cost, and resource usage, researchers were inspired to improve the workflow scheduling (WFS) methods in cloud. Since the randomness of the initial population and the inadequate exploration/exploitation capacity, which has an impact on the quality of the solutions, traditional intelligent optimization algorithms have a poor convergence rate when it comes to resource scheduling. In this research, we suggested a hybridization method combining the Dwarf Mongoose algorithm (DWO) and Chaos opposition based learning algorithm called CO‐DWO in an attempt to develop a more effective optimization algorithm for workflow scheduling issue. The CO‐DWO can be much more advantageous in optimization with a better convergence rate in comparison with other literature techniques.

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