## 1 Introduction  
Cloud computing is based on distributed technology and offers different types of computational resources such as hardware, operating system, and applications to its customers through the Internet on a 24/7 basis and from anywhere in the world [1]. The I.T. giants such as I.B.M., Microsoft, H.P., Google, Amazon, etc., are seen in the cloud market either as a provider or consumer [2]. Cloud technology facilitates its customers to opt for services and reduces the total up-front cost of buying and maintaining the complete infrastructure [3]. There are three main cloud service models: infrastructure as a service (IaaS),software as a service (SaaS), and platform as a service (PaaS) [4] and the cloud services taken from the provider in the form of \"as a service\". Through cloud services, databases can be built and accessed for various research areas, e.g., environmental pollution [5], food authentication [6], and building sector [7; 8]. In addition, the requirements of each customer are different; therefore, a generalized cloud service selection approach will not be efficient as compared to the customized selection approach, which will incorporate an individual's requirements. Moreover, the user requirements also depend on multiple criteria, which are, in most cases, often conflicting in nature [9; 10; 11]. Therefore, selecting cloud services by considers various conflicting criteria at the same time is also a challenge [12]. Cloud services are a tedious part of adopting cloud technology [13; 14]. The performance of any cloud service is measured through the value of its quality of service (QoS) parameters [15]. The Service Measurement Index (S.M.L) [16] has recognized seven QoS attributes and sub-attributes for each cloud service. Some of the attributes mentioned here include reliability, availability, maintainability, agility, accountability, cost, privacy, security, usability, etc. [17]. The QoS attributes are classified into two types: qualitative and quantitative attributes [18; 19], which simplifies the identification and recommendation of the service according to user-customized requirements. The service selection process includes the comparison of the services to each other for finding the best service. If in case there are two or more attributes, then the services should consider all the attributes for comparison. The service selection process is directly proportional to the number of services and the number of attributes. Therefore, if the number of services increases or the number of attributes increases, then it becomes a tedious and time-consuming task to compare the services. Thus, a need for a decision-making system that analyzes the services by considering multiple attributes and recommends the best service to the customer [20]. Evolutionary algorithms [21] are found efficient in finding services in selecting services in case of multi-criteria optimization [22]. Existing work proposed by different researchers using an evolutionary algorithm for selecting cloud services is shown in the work of [23; 24; 25; 26]. Some of the papers consider single or limited QoS attributes [27; 28; 29], while some articles consider multi-criteria [30; 31; 32] for cloud selecting and ranking cloud services. In the cloud, there is an enormous list of available services, and all of these services might not be useful for consideration in the process of service selection [33]. Therefore, if the user requirements are taken in advance, then unnecessary services can be filtered in the initial step itself. Even in the case of multiple criteria, the candidate services can be filtered [34; 35; 36; 37] by considering user requirements for all the mentioned criteria. To support the decision-making process of service selection and recommendation, the contribution in this paper is as follows:  
1. The novel algorithm called cloud service selection and recommendation (CS-SR) is proposed. The algorithm entails four phases, including filtration, evaluation, integration, and last is selection and recommendation.