Biblio

An optimisation is a process of finding maxima or minima of the objective function. Particle Swarm Optimisation (PSO) is a nature-inspired, meta-heuristic, black box optimisation algorithm used to search for global minimum or maximum in the solution space. The sampling strategy in this algorithm mimics the flying pattern of a swarm, where each sample is generated randomly according to uniform distribution among three different locations, which marks the current particle location, the individual best found location, and the best found location for the entire swam over all generation. The PSO has known disadvantage of premature convergence in problems with high correlated design variables (high epistatis). However, there is limited research conducted in finding the main reason why the algorithm fails to locate better solutions in these problems. In this paper, we propose to change the traditional triangular flight trajectory of PSO to an elliptical flight path. The new flying method is tested and compared with the traditional triangular flight trajectory of PSO on five high epistatis benchmark problems. Our results show that the samples generated from the elliptical flight path are generally better than the traditional triangular flight trajectory of PSO in term of average fitness and the fitness of best found solution.