###### Abstract  
Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III. variable length; multi-objective; social class optimization; trust aware; data gathering  
[PERSON], [PERSON]. increasing the amount of collected data may come at the cost of lower accuracy or increased risk of unreliable or malicious sources [9] Correspondingly, improving the accuracy of the data may come at the cost of reducing the amount of data collected. In addition, trust-aware data gathering involves making decisions in a complex and uncertain environment, which makes it challenging to optimize the objectives. The trustworthiness of a source may change over time, or a source may deliberately provide misleading data [10]. Therefore, to address these challenges and optimize conflicting objectives, techniques such as meta-heuristic searching have been extensively used to solve optimization problem with a multiobjective nature [11]. This method starts with the initial setting of random solutions, and it determines the heuristic interactions between the solutions for reaching a set of nondominated solutions after a set of iterations [12]. Multiobjective optimization is evaluated from various perspectives, namely, the Pareto front, hypervolume, delta metric, and generation distance [13]. The quality of the solutions is assessed from various perspectives including domination, diversity, and enabling more choices for the decision-maker [14]. There are numerous algorithms used for various types of meta-heuristics including evolutionary genetic algorithm and particle swarm optimization [15]. However, the meta-heuristic searching algorithm is a specific application that requires developing application-oriented operators that are specifically designed for the problem at hand. These operators are used to generate new candidate solutions during the search process. The effectiveness of these operators can greatly impact the performance of the algorithm and the quality of the solutions obtained [16]. Social class multiobjective particle swarm optimization (SC-MOPSO) is a type of multiobjective particle swarm optimization algorithm that was developed to support variable-length solution spaces [17]. However, SC-MOPSO was developed based on blind mobility operators that do not consider the specific nature of the data-gathering application [18]. Additionally, it has not been extended to be used for multiobjective data gathering with trust-awareness applications using mobile sinks [19]. This study aimed to modify SC-MOPSO for use in multi-mobile sink data-gathering applications with optimal design support capacity.