caa a22 4500 605468818 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1438-y doi (NATIONALLICENCE)springer-10.1007/s00500-014-1438-y Population statistics for particle swarm optimization: Single-evaluation methods in noisy optimization problems [Elektronische Daten] [Juan Rada-Vilela, Mark Johnston, Mengjie Zhang] Particle swarm optimization (PSO) is a metaheuristic whose quality of results deteriorates significantly in optimization problems subject to noise. The underlying reason to such a deterioration is that the effect of noise hinders the ability of particles to distinguish good from bad solutions, leading them to suffer from deception, blindness and disorientation. A deceived particle is not partially attracted to the true best solution in its neighborhood, a blinded particle misses an opportunity to improve upon its personal best solution, and a disoriented particle mistakenly prefers a worse solution. These conditions need to be addressed via noise mitigation mechanisms to prevent (or at least reduce) such a deterioration. Single-evaluation methods are the name by which we refer to PSO algorithms that address the effect of noise without performing additional function evaluations. The first of these algorithms was PSO with evaporation (PSO-E), which was proposed to reduce blindness in the swarms, and reports have suggested that it succeeds at finding better solutions than the regular PSO in different stochastic and dynamic optimization problems. However, PSO-E depends on an evaporation factor whose value is determined empirically, and the swarm is always at risk of exhibiting divergent behaviour. In this article, we propose a method to determine a priori the evaporation factor for PSO-E, and we also propose a new PSO with probabilistic updates (PSO-PU) to prevent the risk of divergence. Additionally, we take a different approach and develop a new PSO with average neighborhoods (PSO-AN) to blur the effect of noise and thereby reduce deception. Experiments on 20 large-scale benchmark functions subject to different levels of noise show that the regular PSO (lacking a noise mitigation mechanism) generally finds better solutions than PSO-E and PSO-PU because their approaches cause too much disorientation. However, PSO-AN finds better solutions than the regular PSO thanks to the improved quality of its neighborhood best solutions that partially attract the swarm towards better regions of the search space. Springer-Verlag Berlin Heidelberg, 2014 Particle swarm optimization nationallicence Population statistics nationallicence Noisy optimization problems nationallicence Single-evaluation methods nationallicence Evaporation mechanism nationallicence Rada-Vilela Juan Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand aut Johnston Mark Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand aut Zhang Mengjie Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2691-2716 1432-7643 19:9<2691 2015 19 500 https://doi.org/10.1007/s00500-014-1438-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1438-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rada-Vilela Juan Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand aut NATIONALLICENCE 700 1- Johnston Mark Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand aut NATIONALLICENCE 700 1- Zhang Mengjie Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2691-2716 1432-7643 19:9<2691 2015 19 500