A comparison of particle swarm optimization and genetic algorithms for a multi-objective Type-2 fuzzy logic based system for the optimal allocation of mobile field engineers

In real world applications it can often be difficult to determine which optimization algorithm to use. This is especially true if the problem has multiple objectives, which is a common occurrence in real world applications. Both Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithms have been explored, often being compared to each other. As problems are scaled up to more objectives, the suitability of these algorithms can change and would need to be modified. The most common multi-objective algorithms in use are Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO), which we are choosing to evaluate, as they can be tested in both their single and multi-objective forms. Real world applications often come with many conditions and constraints. The one being examined in this paper is concerned with the optimal design of working areas, for a large scale mobile workforce in the telecommunications utilities domain. This paper presents the suitable underlying algorithm to use for this problem with the aim of maximizing the utilization of the workforce, whilst having balanced and manageable working areas. The results show that genetic algorithms, in both its single and multi-objective forms, may be the most suitable option for this problem, when compared to PSO and MOPSO algorithms. The results also show that organizing the problem geographically helps the particle swarm algorithms