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A velocity-free fully informed particle swarm optimization algorithm is firstly proposed for multi-objective optimization problems in this paper. It finds the non-dominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. Distinct from other multi-objective PSO, particles in swarm only have position without velocity and all personal...
By combining CMBOA and PSO with quasi-oppositional learning, a cloud estimation of distribution particle swarm optimizer is firstly introduced. Then, it is extended to multi-objective optimization problems by using maximum ranking. In the algorithm's offspring generation scheme, new individuals are generated in the cloud estimation of distribution way or in the PSO way. And instead of Pareto dominance,...
Some particle swarm optimization(PSO) algorithms have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. In this paper, we propose a new opposition-based learning fully informed particle swarm optimizer with favour ranking to solve multi-objective optimization problems. Instead of Pareto dominance, favour ranking is used to identify...
Cloud estimation of distribution particle swarm optimizer combining PSO and cloud model is introduced. In the algorithm's offspring generation scheme, new particles are generated in the cloud estimation of distribution way or in the PSO way. The innovation of the algorithm is production of cloud particles according to the cloud model theory. The cognitive population obtained during optimization is...
A new multi-objective estimation of distribution algorithm combined with PSO by using a Pareto-based method is proposed and applied in RFID network design. In the algorithm's offspring generation scheme, one part of individuals is sampled in the search space from the constructed probabilistic distribution model and the other part individuals are generated by the velocity-free PSO. A balance parameter...
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