## 1 Introduction
Lately, PSO has been used to solve real-world engineering problems such as the design of multilayered rectangular microstrip antenna using electromagnetic band-gap (EBG) structures [29], and bandwidth improvement of an inverted-F antenna (IFA) [30]. Furthermore, the PSO algorithm is also able to design new engineering components, for example, artificial magnetic conductors [31]. Apart from optimizing solutions based on bioinspired algorithms, the multiobjective optimization (MOO) algorithm is designed to solve the most challenging concern of combinatorial optimization problems, as well as of network applications. MOO algorithm is known as Pareto optimization, and it is classified as a multiple-criteria decision analysis (MCDA). The MOO algorithm is used in connection with optimization problems that include more than one conflicting objective function to be optimized simultaneously [32]. Multiobjective problems, such as routing in communication networks [33;34], compressor design [35;36], engineering [37;38], and logistics [39], require a number of objective functions for simultaneous optimization [40]. It is essential to solve the system's task scheduling problem to increase resource utilization and sharing rate in distributed systems. Task scheduling became more difficult and complex for parallel and distributed environment due to resource distribution, heterogeneity, and autonomy. Different research projects have been carried out to improve the ant colony optimization performance, for example, a novel pheromone update strategy [41; 42], a modified ant colony optimization with improved tour construction and pheromone updating strategies [43], an AntNet with reward-penalty reinforcement learning [44], and an AntNet routing technique for real-world application [45]. A further improvement on the original form of the ant system (AS) is called the elitist strategy (elitist-AS) [46]. The concept is to provide strong reinforcement at the edges of the optimal path determined at the beginning of the algorithm. Using the elitist method with an appropriate number of elitist ants enables AS to locate better routes earlier in the run. Despite the existing researches for ACO algorithm can improve the efficiency of task scheduling, they do not contribute significantly to improving the overall load imbalance of the system. In this paper, a novel ACO-NN approach is proposed to manage task assignment of load balancing system. The developed algorithm (ACO-NN) meets the following main contributions:  
1. Take into consideration the system heterogeneity and the dynamic task scheduling;
2. Carry out different computing data sets and experiments to investigate load balancing performance;
3. Minimize the total cost of the computing system;
4. Validate the obtained results with the results of previous research using makespan and the optimal solution as performance measures. The structure of this paper is organized as follows. Section 2 introduces the hybrid of nearest neighbor and ACO algorithm. In Section 3, various experiments are presented, and the outcomes of the experiments are analyzed. Section 4 carries out the discussion of comparison tests. Finally, conclusion is provided in Section 5.