The flexi-PSO: Towards a more flexible particle swarm optimizer

This paper presents some simple heuristics to increase flexibility in particle swarms. The particle velocities are updated with an extended inertia weight formulation, particles are also allowed to act as hill-climbers and the population is divided into exploration particles which are allowed to roam the search space more freely and exploitation particles which try to improve the fitness by fine tuning a local search, thus making the swarm much more flexible in its behaviour. These heuristics improve the traditional inertia weight PSO and show comparable performance in the computational results of benchmark functions to other state of the art techniques. The benchmark functions used are rigorous test bed for evaluating any optimization algorithm. An introduction and general overview of approaches to particle swarm optimizers is first presented, followed by a discussion of the proposed method. The result of the experiments is then discussed followed by proposals for future research directions.