Grammatical Swarm Based-Adaptable Velocity Update Equations in Particle Swarm Optimizer

Si, Tapas; De, Arunava; Bhattacharjee, Anup Kumar

doi:10.1007/978-3-319-02931-3_24

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…In a similar approach, Si et al [13] replaced GP by GDE (Grammatical Differential Evolution [14]) and also achieved better performance than the PSO version used in comparison.…”

Section: Related Workmentioning

confidence: 99%

Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization

Melo

Banzhaf

2017

Neural Comput & Applic

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Abstract-This paper proposes Drone Squadron Optimization, a new self-adaptive metaheuristic for global numerical optimization which is updated online by a hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many algorithms used nowadays, which are nature-inspired. DSO is very flexible because it is not related to behaviors or natural phenomena. DSO has two core parts: the semi-autonomous drones that fly over a landscape to explore, and the Command Center that processes the retrieved data and updates the drones' firmware whenever necessary. The self-adaptive aspect of DSO in this work is the perturbation/movement scheme, which is the procedure used to generate target coordinates. This procedure is evolved by the Command Center during the global optimization process in order to adapt DSO to the search landscape. DSO was evaluated on a set of widely employed benchmark functions. The statistical analysis of the results shows that the proposed method is competitive with the other methods in the comparison, the performance is promising, but several future improvements are planned.

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“…In a similar approach, Si et al [13] replaced GP by GDE (Grammatical Differential Evolution [14]) and also achieved better performance than the PSO version used in comparison.…”

Section: Related Workmentioning

confidence: 99%

Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization

Melo

Banzhaf

2017

Neural Comput & Applic

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“…Given an optimization problem, we aimed to generate a configuration of parameters (i.e., a design) for the PSO algorithm that is adequate in terms of optimization performance. We can identify in the literature previous works that performed empirical analysis on specific PSO parameters in isolation, which can be useful in practice [16,17,14,4,20]. However, few attempts have been made to generate more complete designs that could for instance be systematically reused across similar problems.…”

Section: Introductionmentioning

confidence: 97%

“…In the context of PSO design, [20] adopted GE techniques to adjust the velocity equation, similarly to [17,14]. However, the potential advantages of GE were not properly leveraged in [20], since it was also limited to evolve velocity updating equations. Other issues of PSO (e.g., the topology adopted) are important to convergence and quality of its solutions.…”

Section: Introductionmentioning

confidence: 99%

Gefpso

Miranda

Prudêncio

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In this work, we propose a framework to automatically generate effective PSO designs by adopting Grammatical Evolution (GE). In the proposed framework, GE searches for adequate structures and parameter values (e.g., acceleration constants, velocity equations and different particles' topology) in order to evolve the PSO design. For this, a highlevel Backus-Naur Form (BNF) grammar was developed, representing the search space of possible PSO designs. In order to verify the performance of the proposed method, we performed experiments using 16 diverse continuous optimization problems, with different levels of difficulty. In the performed experiments, we identified the parameters and components that most affected the PSO performance, as well as identified designs that could be reused across different problems. We also demonstrated that the proposed method generates useful designs which achieved competitive solutions when compared to well succeeded algorithms from the literature.

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“…T. Si [6] used Grammatical Differential Evolution(GDE) to evolve each particle's velocity update equation in order to create the diversity in the population. T. Si et al [7] used Grammatical Swarm (GS) to evolve each particle's velocity update equation for the same purpose. Different variants of PSO is also developed for performance improvement.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization with generalized opposition based learning in particle's pbest position

De²,

Bhattacharjee

2014

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]

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This paper presents an improved Particle Swarm Optimizer with opposition based learning method. The key feature of this method is that opposition based learning scheme is employed in personal best position of particles called pbest position in order to improve the performance of particle swarm optimizer. The proposed method is termed as OpbestPSO. OpbestPSO is applied on 12 benchmark problems. The experimental results shows the better performance of the proposed method OpbestPSO.

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Grammatical Swarm Based-Adaptable Velocity Update Equations in Particle Swarm Optimizer

Cited by 11 publications

References 10 publications

Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization

Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization

Gefpso

Particle swarm optimization with generalized opposition based learning in particle's pbest position

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