A rank based particle swarm optimization algorithm with dynamic adaptation

Akbari, Reza; Ziarati, Koorush

doi:10.1016/j.cam.2010.11.021

Cited by 61 publications

(74 citation statements)

References 17 publications

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“…Their definitions as well as source codes can be obtained through online sources. 1 Comparative results for different algorithms are also publicly available. 2 PSO-NBA was applied on the 50-and 100-dimensional instances of the test problems, adopting the experimental setting in [14].…”

Section: Further Experimentsmentioning

confidence: 99%

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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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Section: Further Experimentsmentioning

confidence: 99%

“…There are also works that propose rank-based PSO variants in the literature. In [1] the presented algorithm uses only a fraction of the particles to update velocity. This approach is solely based on the global (gbest) PSO model, neglecting the neighborhoods.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization with neighborhood-based budget allocation

Souravlias

Parsopoulos

2014

Int. J. Mach. Learn. & Cyber.

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“…A comprehensive list of many of the variants can be found in [21]. The variants adopted for comparison in this work are Rank based PSO (PSO rank ) in [1], Adaptive Inertia Weight PSO (AIWPSO) in [18], Adaptive PSO (APSO) in [2], Natural Exponential inertia weight PSO (e 1 -PSO) in [7], Decreasing exponential function PSO (def-PSO) in [8] and Chaotic Random Inertia Weight PSO (CRIW-PSO) in [10]. All these variants have proved to be superior to their competitors in their various capacities.…”

Section: A Pso Variantsmentioning

confidence: 99%

“…Over the years, many researchers have made tremendous efforts to improve on the effectiveness, efficiency and robustness of PSO technique in solving optimization problems. Researches in this direction are the introduction of V max [6], inertia weight [23], constriction factor [4] into PSO, as well as improvements on the inertia weight [1,3,7,8,11,17,18,22,24], PSO with mutation operators [2,5,11,15,16], hybridization of PSO with other algorithms [19] and development of other variants of PSO [21]. Despite these improvements on PSO, virtually none of the existing variants of PSO have been able to solve optimization problems with high dimension up to 2000.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization

Arasomwan¹,

Adewumi²

2013

2013 IEEE Congress on Evolutionary Computation

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Researchers have achieved varying levels of successes in proposing different methods to modify the particle's velocity updating formula for better performance of Particle Swarm Optimization (PSO). Variants of PSO that solved highdimensional optimization problems up to 1,000 dimensions without losing superiority to its competitor(s) are rare. Meanwhile, high-dimensional real-world optimization problems are becoming realities hence PSO algorithm therefore needs some reworking to enhance it for better performance in handling such problems. This paper proposes a new PSO variant called Adaptive Velocity PSO (AV-PSO), which adaptively adjusts the velocity of particles based on Euclidean distance between the position of each particle and the position of the global best particle. To avoid getting trapped in local optimal, chaotic characteristics was introduced into the particle position updating formula. In all experiments, it is shown that AV-PSO is very efficient for solving low and high-dimensional global optimization problems. Empirical results show that AV-PSO outperformed AIWPSO, PSO rank , CRIW-PSO, def-PSO, e1-PSO and APSO. It also performed better than LSRS in many of the tested high-dimensional problems. AV-PSO was also used to optimize some high-dimensional problems with 4,000 dimensions with very good results.

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“…The neural network classifier is trained using particle swarm optimization algorithm. In [7,21,22] the corresponding authors demonstrate hybrid PSO based neural network training algorithm. A population based search algorithm is used to search out optimized synaptic weights for a multilayer perceptron.…”

Section: Neuro Swarm Optimization Techniquementioning

confidence: 99%

Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture

Ojha

Dutta

2012

AIR

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The article presents performance analysis of the neuro swarm optimization algorithm applied for the detection of proportion of the component gases found in manhole gas mixture. The hybrid neuro swarm optimization technique is used for implementing an intelligent sensory system for the detection of component gases present in manhole gas mixture. The manhole gas mixture typically contains toxic gases such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, and Carbon Monoxide. A semiconductor based gas sensor array used for sensing the gas components consists of many sensor elements, where each sensor element is responsible for sensing particular gas component. Presence of multiple gas sensors for detecting multiple gases results in cross-sensitivity. The central theme of this article is the performance analysis of the algorithm which offers solution to multiple gas detection issue. The article also presents study on the computational cost incurred by the algorithm.

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A rank based particle swarm optimization algorithm with dynamic adaptation

Cited by 61 publications

References 17 publications

Particle swarm optimization with neighborhood-based budget allocation

Particle swarm optimization with neighborhood-based budget allocation

An Adaptive Velocity Particle Swarm Optimization for high-dimensional function optimization

Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture

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