Particle Swarm optimization (PSO) is a stochastic optimization approach that originated from early attempts to simulate the behavior of birds looking for food. Estimation of distributions algorithms (EDAs) are a class...
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ISBN:
(纸本)9781467315098
Particle Swarm optimization (PSO) is a stochastic optimization approach that originated from early attempts to simulate the behavior of birds looking for food. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms that build and maintain a probabilistic model capturing the search space characteristics and continuously use this model to generate new individuals. In this work, we propose a new PSO and EDA hybrid algorithm that uses the particles' distribution in the search space in order to adjust the search space bounds, hence, restricting the particles movement as well as their allowable maximum velocity. The algorithms is augmented with a mechanism to overcome premature convergence and escape local minima. The algorithm is compared to the standard PSO algorithm using a suite of well-known benchmark optimizationfunctions. Experimental results show that the proposed algorithm has a promising performance.
Particle Swarm optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation o...
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Particle Swarm optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known benchmark optimizationfunctions.
This paper investigates the idea of having multiple swarms working separately and cooperating with each other to solve an optimization problem. Many factors that influence the behavior of this approach haven't bee...
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ISBN:
(纸本)1595930108
This paper investigates the idea of having multiple swarms working separately and cooperating with each other to solve an optimization problem. Many factors that influence the behavior of this approach haven't been properly studied. This paper investigates two factors that affect this approach behavior. These factors are: (i) the communication strategy adopted if the number of swarms is raised above two, and (ii) the number of cooperating swarms. Experiments run on different benchmark optimizationfunctions show that adopting a circular communication strategy gives better results than just sharing the global best of all the swarms. Increasing the number of cooperating swarms provides better results provided that the appropriate synchronization period is selected.
This paper investigates the idea of having multiple swarms working separately and cooperating with each other to solve an optimization problem. Many factors that influence the behavior of this approach haven't bee...
详细信息
ISBN:
(纸本)9781595930101
This paper investigates the idea of having multiple swarms working separately and cooperating with each other to solve an optimization problem. Many factors that influence the behavior of this approach haven't been properly studied. This paper investigates two factors that affect this approach behavior. These factors are: (i) the communication strategy adopted if the number of swarms is raised above two, and (ii) the number of cooperating swarms. Experiments run on different benchmark optimizationfunctions show that adopting a circular communication strategy gives better results than just sharing the global best of all the swarms. Increasing the number of cooperating swarms provides better results provided that the appropriate synchronization period is selected.
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