## 1 Introduction  
The new global technological revolution and industrial changes have a huge impact on the manufacturing industry, which is developing in the direction of \"digitalization, networking, personalization, collaboration, agility, service, green and intelligence\" [1]. In this context, academic [PERSON] and his team started to further develop the manufacturing industry in 2009 [2]. His team proposed the concept, mode, technical means, and industry of \"cloud manufacturing\" and started researching the cloud manufacturing mode with networking and service as the main features. In the manufacturing industry, manufacturing resources and manufacturing capabilities are managed in a unified manner, and the demander can perform the full-process response manufacturing tasks in sequence from the cloud platform according to diverse needs at any time and place [3]. Due to the unified management and allocation of manufacturing resources and manufacturing capabilities, the utilization of resources increases, and the usage of different resources differs, so the rational scheduling of resource use has also become a hot topic of research. Due to the numerous resources and services in the cloud platform, it is relatively difficult to obtain an efficient, fast, and accurate solution. In view of the production scheduling requirements of the cloud manufacturing workshop layer, this paper further studies the flexible job shop scheduling problem, which is an extension of the traditional job shop scheduling problem [4]. Allocating limited manufacturing resources to tasks with specific performance metrics ensures that the correct resources can be efficiently allocated to execute work items/tasks with special needs in a reasonable time [5; 6], ensuring a balance between demand for manufacturing capacity and resource availability [2]. On a specific machine, corresponding tasks can be processed in a specific order/sequence. This increases the flexibility of workshop scheduling, which is more in line with actual production and is a type of scheduling problem that urgently needs to be solved. Furthermore, we will delve into the latest applications of the brainstorming optimization (BSO) algorithm, the dual-objective SA-LP (IMOSSA/D) algorithm, and the improved genetic algorithm (IGA) [7; 8; 9; 10; 11]. The particle swarm intelligence and genetic algorithm SPGA [12], improved hybrid genetic algorithm and variable neighbor search (HGA-VNS) [13], multi-search algorithm genetic algorithm, novel energy-aware estimation model, improved Dragonfly algorithm (DOLDA), multi-agent reinforcement learning (MARL) [7; 8; 9; 14], multi-objective evolutionary algorithm (EMOEA), improved Antilion optimization algorithm (DALO), improved artificial jellyfish search algorithm (JS), and marine predator algorithm (MPA) [15; 16] can be utilized to solve complex large-scale flexible workshop job scheduling problems. Compared with the traditional job shop scheduling problem, the flexible job shop scheduling problem reduces machine constraints, increases the uncertainty of a specific machine, and expands the search range of feasible solutions. It is a more complex NP-Hard problem. At present, intelligent algorithms such as genetic algorithms (GAs) and artificial bee colonies have been widely used to solve the component optimization problem algorithm (ABC). Simulated annealing (SA), taboo search (TS), and the particle swarm algorithm (PSO) are used in the cloud environment due to their fast search speed [6; 17; 18; 19]. An algorithm called the Chimp optimizer (ChOA) was proposed by [PERSON] et al. [20]. Inspired by the predatory behavior of chimps, the ChOA has been successfully applied to other fields due to its significant advantages, such as strong convergence performance, few parameters, and easy implementation. Due to the large and complex search space of ChOA, its advantages are of great importance for task scheduling in CMFG.