Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

Heterogeneous networks (HetNets) are a promising means of meeting the requirements of Long Term Evolution-Advanced (LTE-A) in terms of data traffic, coverage and capacity. In HetNets, power disparities arise between base stations in different tiers. The use of existing user association schemes will lead to load imbalances between these base stations, thus affecting network performance. Biased user association has been widely studied to improve load balancing in HetNets. Static biasing has been the focus of most existing work but this approach does not yield optimized performance because the optimal biasing values vary with user location. In this paper, we investigate the use of the Particle Swarm Optimization (PSO) algorithm to conduct dynamic user association by finding the optimal bias values. The simulation results demonstrate that the proposed scheme achieves better load balancing performance in terms of the network balance index compared to a baseline scheme.

Heterogeneous networks (HetNets) are a promising communication paradigm to satisfy the diverse requirements of Long Term Evolution-Advanced (LTE-A). Associating users with different base station tiers using the conventional technique based on the highest received SINR is not viable in HetNets due to its rigid association, which only aims at throughput maximization. Many e orts have been made to tackle the optimization problem of user association with a single objective such as throughput, fairness or energy efficiency. In this paper, we propose a novel multi-objective user association technique using particle swarm optimization (PSO) with the aim of jointly maximizing the throughput and the network balance index (NBI). By incorporating weight factors into the proposed scheme, the system operator has the flexibility to configure the priority levels of throughput and NBI. Numerical results demonstrate that our proposed multi-objective user association technique achieves better performance in terms of fitness values compared to the single-objective user association schemes.