Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted  investigates the use of different methods for estimating the probability distributions used in the update rule of Bare Bones PSO.Four updating strategies are introduced for updating particle positions in Bare Bones PSO that are using normal or multivariate normal distributions.A novel covariance matrix adaption scheme is introduced for updating the covariance matrix of the multivariate Gaussian distribution.A strategy selection mechanism based on cellular learning automata is proposed for the selection of an appropriate updating strategy based on the problem properties and evolutionary stage of the algorithm's execution.In order to demonstrate the characteristics of the proposed method, comparisons with some other PSO algorithms from the literature are performed. Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. Hover, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense impact on the performance of the bare bones PSO. As a result, this paper investigates the use of different methods for estimating the probability distributions used in the update rule. Four methods or strategies are developed that are using Gaussian or multivariate Gaussian distributions. The choice of an appropriate updating strategy for each particle greatly depends on the characteristics of the fitness landscape that surrounds the swarm. To deal with issue, the cellular learning automata model is incorporated with the proposed bare bones PSO, which is able to adaptively learn suitable updating strategies for the particles. Through the interactions among its elements and the learning capabilities of its learning automata, cellular learning automata gradually learns to select the best updating rules for the particles based on their surrounding fitness landscape.  also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies. The proposed method is compared with some other ll-known particle swarm approaches. The results indicate the superiority of the proposed approach in terms of the accuracy of the achieved results and the speed in finding appropriate solutions.
['Reza Vafashoar', 'Mohammad Reza Meybodi']
I am writing a research paper on a idea Multi swarm bare bones particle swarm optimization with distribution adaption
[{"from": "human", "value": "I am writing a research paper on a idea Multi swarm bare bones particle swarm optimization with distribution adaption"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted  investigates the use of different methods for estimating the probability distributions used in the update rule of Bare Bones PSO.Four updating strategies are introduced for updating particle positions in Bare Bones PSO that are using normal or multivariate normal distributions.A novel covariance matrix adaption scheme is introduced for updating the covariance matrix of the multivariate Gaussian distribution.A strategy selection mechanism based on cellular learning automata is proposed for the selection of an appropriate updating strategy based on the problem properties and evolutionary stage of the algorithm's execution.In order to demonstrate the characteristics of the proposed method, comparisons with some other PSO algorithms from the literature are performed. Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. Hover, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense impact on the performance of the bare bones PSO. As a result, this paper investigates the use of different methods for estimating the probability distributions used in the update rule. Four methods or strategies are developed that are using Gaussian or multivariate Gaussian distributions. The choice of an appropriate updating strategy for each particle greatly depends on the characteristics of the fitness landscape that surrounds the swarm. To deal with issue, the cellular learning automata model is incorporated with the proposed bare bones PSO, which is able to adaptively learn suitable updating strategies for the particles. Through the interactions among its elements and the learning capabilities of its learning automata, cellular learning automata gradually learns to select the best updating rules for the particles based on their surrounding fitness landscape.  also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies. The proposed method is compared with some other ll-known particle swarm approaches. The results indicate the superiority of the proposed approach in terms of the accuracy of the achieved results and the speed in finding appropriate solutions."}]