# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 1 Introduction
Meta-heuristic algorithms, such as particle swarm optimization (PSO), have been used to resolve task scheduling problems [13; 14; 15]. PSO was first discovered by [PERSON] [16], who was inspired by the social behavior of animals. PSO has many advantages [17], including the simplicity in its concepts, its durability of parameters, being easy to implement, being able to achieve the optimal solution quickly, and being fast in terms of controlling the  
Figure 1: A classification of spatial crowdsourcing. Reward = reward-based worker; voluntary = voluntary-based worker; SAT = server-assigned task; WST = worker-selected task; single = single worker; multiple = multiple workers. computation. Additionally, PSO has the ability to find an optimal solution in different applications. For instance, it was extended to multi-objective particle swarm optimization (MOPSO) in order to solve MOO effectively by [18] when merging the advantages of PSO and MOO. This research aims to find an optimal multi-objective task scheduling approach based on the three conflicting objectives mentioned previously. Thus, we propose the multi-objective task scheduling optimization (MOPSO) model, which involves two primary novel algorithms. The first algorithm is based on a relatively new fitness function, which was introduced in detail in our previous work [19], while the second algorithm is the ranking strategy algorithm, which is discussed in this paper. The main goal of the ranking strategy algorithm is to improve and enhance the performance of our MOPSO. The motivation for this work stems from the fact that in recent years, many governments have become interested in geographical or spatial tasks such as gathering geographic data and measuring spatial data within SC [5]. Moreover, geographical information has great importance to people's daily lives [20]. Thus, there are many applications that are used for different purposes, e.g., Uber [21], which is a taxi ride-sharing application; Google Maps [22] and Waze [23], which are GPS-based geographical navigation applications that allow drivers to share traffic and road information in a quick and easy manner; and Yelp [24], which is a food discovery and delivery service. Other successful examples include TaskRabbit [25], which provides household tasks and home services, and Gigwalk [26], which is a marketplace for sensing tasks such as geo-images, videos, or collecting answers about stores. These are only some examples of the available applications, as there are many other applications with a range of purposes. Despite the attention paid to the SC field, most of the previous research in this area did not address the TS-MOO problem, which deals with the three conflicting objectives in SC. Our motivation is based on the fact that there is a need for a method that addresses the TS-MOO problem in SC, in order to determine the optimal scheduling for a task based on the three primary conflicting objectives mentioned, which are adopted in this study and treated with equal importance. An additional example is the accident reporting application to manage accident-related activities; in more detail, when a traffic accident occurs and there are no casualties, one party (the requester) will send a request to the platform. The platform (server) will then assign this task to a suitable worker; the worker must attend the location of the accident to investigate and will receive the details related to the accident in order to determine the responsibility between all parties involved. Then, the worker will issue an accident report that contains all of the details (such as a photograph of the accident and damage evaluation reports) to all parties involved in the incident. This platform aims to assist the traffic police in order to enhance road safety and to provide better monitoring for field operations. Figure 2 shows a certain scenario that we have set up for SC. In Figure 2, \(t_{j}~{}=~{}\left<I_{t_{j}},d_{t_{j}}\right>\)\(l_{t_{j}}\) is the location of the task, and \(d_{t_{j}}\) is the time duration of a task.