Multi-objective optimisation using sharing in swarm optimisation algorithms

Many problems in the real world are multi-objective by nature, this means that many times there is the need to satisfy a problem with more than one goal in mind. These type of problems have been studied by economists, mathematicians, between many more, and recently computer scientists. Computer scientists have been developing novel methods to solve this type of problems with the help of evolutionary computation. Particle Swarm Optimisation (PSO) is a relatively new heuristic that shares some similarities with evolutionary computation techniques, and that recently has been successfully modified to solve multi-objective optimisation problems. In this thesis we first review some of the most relevant work done in the area of PSO and multi-objective optimisation, and then we proceed to develop an heuristic capable to solve this type of problems. An heuristic, which probes to be very competitive when tested over synthetic benchmark functions taken from the specialised literature, and compared against state-of-the-art techniques developed up to this day; we then further extended this heuristic to make it more competitive. Almost at the end of this work we incursion into the area of dynamic multi-objective optimisation, by testing the capabilities and analysing the behaviour of our technique in dynamic environments