Simulation optimization using the Particle Swarm Optimization with optimal computing budget allocation

Zhang, Bo; Chen, Guihai; Lee, Loo Hay; Peng, Peng

doi:10.1109/wsc.2011.6148117

Cited by 25 publications

(53 citation statements)

References 16 publications

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“…The experiments on these functions in 2-6 dimensions ranked PSO-OCBA with hypothesis testing and PSO-OCBA first and second, respectively, based on the average objective function values of the solutions found. Further studies on this topic have confirmed the superior quality of the solutions found utilizing PSO-OCBA ( Bartz-Beielstein et al 2007;Zhang et al 2011;Rada-Vilela et al 2013a, b). Bartz-Beielstein et al (2007) explored the performance of the regular PSO under the assumptions of local and global certainty, and also compared the quality of the results obtained with PSO-OCBA (Pan et al 2006), PSO-ER and the regular PSO.…”

Section: Local and Global Certaintymentioning

confidence: 81%

“…-Study the population statistics for particle swarms with resampling methods explicitly designed to reduce deception (i.e., maximize the probability of correct selection) such as PSO-OCBA (Pan et al 2006;Bartz-Beielstein et al 2007;Zhang et al 2011;Rada-Vilela et al 2013a) and those based on reinforcement learning by Piperagkas et al (2012). -Study the population statistics for particle swarms with evaporation mechanisms designed to reduce blindness (Cui et al 2005(Cui et al , 2009Cui and Potok 2007;Arcos 2009, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Deception is a condition that has been previously addressed in the literature as the problem of correctly selecting the neighborhood best solution (Pan et al 2006;Bartz-Beielstein et al 2007;Zhang et al 2011;Piperagkas et al 2012;Rada-Vilela et al 2013a, b).…”

Section: Deceptionmentioning

confidence: 99%

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Deception, blindness and disorientation in particle swarm optimization applied to noisy problems

2014

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Particle swarm optimization (PSO) is a population-based algorithm designed to find good solutions to optimization problems. However, if the problems are subject to noise, the quality of its results significantly deteriorates. Previous works have addressed such a deterioration by developing noise mitigation mechanisms to target specific issues such as handling the inaccurate memories of the particles and aiding the particles to correctly select their neighborhood best solutions. However, in spite of the improvements achieved, it still remains uncertain the extent to which these issues affect the particles, and the underlying reasons for the deterioration of the quality of the results. In this article, we formally define deception, blindness and disorientation as the conditions responsible for such a deterioration, and we develop a set of population statistics to measure the extent to which these conditions affect the particles throughout the search process. The population statistics are computed for the regular PSO algorithm and for PSO with equal resampling (PSO-ER) on 20 large-scale benchmark functions subject to different levels of multiplicative Gaussian noise. The key findings that we reveal with the population statistics on optimization problems subject to noise are the following: (a) the quality of the results significantly deteriorates as particles suffer from large proportions of deception and blindness; (b) the presence of deception, blindness and disorientation, and their effects on the quality of results, makes the performance Electronic supplementary material The online version of this article (123 248 Swarm Intell (2014) 8:247-273of the swarms sensitive to optimization problems subject to noise; (c) the incorporation of resampling methods into PSO significantly improves the quality of the results; and (d) it is better to first address the conditions of blindness and disorientation before addressing deception.

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Section: Local and Global Certaintymentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

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Deception, blindness and disorientation in particle swarm optimization applied to noisy problems

2014

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“…Based on the use of the computational budget of function evaluations, the literature has distinguished two conceptually different approaches to mitigate the effect of noise on PSO. On the one hand, resampling-based PSO algorithms [17] allocate multiple function evaluations to the solutions in order to better estimate their objective function values by a sample mean over the evaluations [18][19][20][21]. On the other hand, single-evaluation PSO algorithms [22] do not allocate additional function evaluations to the solutions and focus instead on reducing the effect of having solutions with very inaccurately estimated objective function values [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Population statistics for particle swarm optimization: Hybrid methods in noisy optimization problems

Rada-Vilela

Johnston

Zhang

2015

Swarm and Evolutionary Computation

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a b s t r a c tParticle swarm optimization (PSO) is a metaheuristic designed to find good solutions to optimization problems. However, when optimization problems are subject to noise, the quality of the resulting solutions significantly deteriorates, hence prompting the need to incorporate noise mitigation mechanisms into PSO. Based on the allocation of function evaluations, two opposite approaches are generally taken. On the one hand, resampling-based PSO algorithms incorporate resampling methods to better estimate the objective function values of the solutions at the cost of performing fewer iterations. On the other hand, single-evaluation PSO algorithms perform more iterations at the cost of dealing with very inaccurately estimated objective function values. In this paper, we propose a new approach in which hybrid PSO algorithms incorporate noise mitigation mechanisms from the other two approaches, and the quality of their results is better than that of the state of the art with a few exceptions. The performance of the algorithms is analyzed by means of a set of population statistics that measure different characteristics of the swarms throughout the search process. Amongst the hybrid PSO algorithms, we find a promising algorithm whose simplicity, flexibility and quality of results questions the importance of incorporating complex resampling methods into state-of-the-art PSO algorithms.

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“…In [4,17,21,33] PSO was equipped with the optimal computational budget allocation (OCBA) method in order to cope with optimization problems contaminated by noise. These works differ from the present study since we cope with noiseless problems and propose a more general budget allocation scheme.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization with neighborhood-based budget allocation

Souravlias

Parsopoulos

2014

Int. J. Mach. Learn. & Cyber.

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The standard particle swarm optimization (PSO) algorithm allocates the total available budget of function evaluations equally and concurrently among the particles of the swarm. In the present work, we propose a new variant of PSO where each particle is dynamically assigned different computational budget based on the quality of its neighborhood. The main goal is to favor particles with high-quality neighborhoods by asynchronously providing them with more function evaluations than the rest. For this purpose, we define quality criteria to assess a neighborhood with respect to the information it possesses in terms of solutions' quality and diversity. Established stochastic techniques are employed for the final selection among the particles. Different variants are proposed by combining various quality criteria in a singleor multi-objective manner. The proposed approach is assessed on widely used test suites as well as on a set of real-world problems. Experimental evidence reveals the efficiency of the proposed approach and its competitiveness against other PSO-based variants as well as different established algorithms.

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Simulation optimization using the Particle Swarm Optimization with optimal computing budget allocation

Cited by 25 publications

References 16 publications

Deception, blindness and disorientation in particle swarm optimization applied to noisy problems

Deception, blindness and disorientation in particle swarm optimization applied to noisy problems

Population statistics for particle swarm optimization: Hybrid methods in noisy optimization problems

Particle swarm optimization with neighborhood-based budget allocation

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