Pamparà, GaryEngelbrecht, Andries P.2018-12-032018-10Pamparà G., Engelbrecht A.P. (2018) Self-adaptive Quantum Particle Swarm Optimization for Dynamic Environments. In: Dorigo M., Birattari M., Blum C., Christensen A., Reina A., Trianni V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science, vol 11172. Springer, Cham.0302-9743 (print)1611-3349 (online)10.1007/978-3-030-00533-7_13http://hdl.handle.net/2263/67434The quantum-inspired particle swarm optimization (QPSO) algorithm has been developed to find and track an optimum for dynamic optimization problems. Though QPSO has been shown to be effective, despite its simplicity, it does introduce an additional control parameter: the radius of the quantum cloud. The performance of QPSO is sensitive to the value assigned to this problem dependent parameter, which basically limits the area of the search space wherein new, better optima can be detected. This paper proposes a strategy to dynamically adapt the quantum radius, with changes in the environment. A comparison of the adaptive radius QPSO with the static radius QPSO showed that the adaptive approach achieves desirable results, without prior tuning of the quantum radius.en© Springer Nature Switzerland AG 2018. The original publication is available at : http://link.springer.combookseries/558.Swarm intelligenceAdaptive approachAdditional controlDynamic environmentsDynamic optimization problem (DOP)Search spacesSelf-adaptiveParticle swarm optimization (PSO)Quantum-inspired particle swarm optimization (QPSO)Postprint Article