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RFID networks planning using a multi-swarm optimizer
2009 Chinese Control and Decision Conference, 2009In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel Multi-swarm Particle Swarm Optimizer called PS2O. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update ...
Hanning Chen, Yunlong Zhu, Kunyuan Hu
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Hierarchical multi-swarm cooperative teaching–learning-based optimization for global optimization
Soft Computing, 2016Hierarchical cooperation mechanism, which is inspired by the features of specialization and cooperation in the social organizations, has been successfully used to increase the diversity of the population and avoid premature convergence for solving complex optimization problems.
Feng Zou +3 more
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Multi- Swarm and Multi- Best particle swarm optimization algorithm
2008 7th World Congress on Intelligent Control and Automation, 2008This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until ...
null Junliang Li, null Xinping Xiao
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Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search
2005 IEEE Congress on Evolutionary Computation, 2005In this paper, the performance of a modified dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided by CEC2005 is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these
J.J. Liang, P.N. Suganthan
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Optimizing fuzzy membership function using dynamic multi swarm — PSO
2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016Performance of fuzzy application to solve the control problems depends on a number of parameters such as the choice and shape of the membership function. Defining MFs manually in a proper way is time consuming, prone to errors and difficult. And especially it depends subjectively based on expert's experiences.
Animesh Kumar Paul, Pintu Chandra Shill
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Multi-swarm particle swarm optimization with multiple learning strategies
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014Inspired by the division of labor and migration behavior in nature, this paper proposes a novel particle swarm optimization algorithm with multiple learning strategies (PSO-MLS). In the algorithm, particles are divided into three sub-swarms randomly while three learning strategies with different motivations are applied to each sub-swarm respectively ...
Meng-Qi Peng +3 more
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Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services
Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2021T. Mohanraj, R. Santhosh
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Multi swarm optimization based automatic ontology for e-assessment
Computer Networks, 2019Abstract The utilization of ontology in the e-assessment area has grown tremendously. The context of e-learning is significant to the students for educational purposes. This makes the testing process easy for the students and also for the teachers. The majority of the approaches that deals with the ontology issue have suggested that the individual ...
A. Santhanavijayan, S.R. Balasundaram
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Properties of Quantum Particles in Multi-Swarms for Dynamic Optimization
Fundamenta Informaticae, 2009This paper studies properties of a multi-swarm system based on a concept of physical quantum particles (mQSO). Quantum particles differ from the classic ones in the way they move. As opposed to the classic view of particle movement, where motion is controlled by linear kinematic laws, quantum particles change their location according to random ...
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