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Up to now, classical Quantum Particle Swarm Optimization Algorithm in the late period of convergence has showed some drawbacks, such as population diversity reduce, convergence speed slow down and easy to fall into local optimal solution. This paper improves the classic QPSO algorithm and proposes Grouped Quantum-inspired Particle Swarm Optimization (G-QPSO). In this algorithm, quantum particles are...
Optimization problems with more than three objectives, i.e., many-objective problems (MaOPs), have gained more and more attentions in the field of evolutionary multi-objective optimization (EMO) in that the powerful Pareto comparisons and evolutionary strategies are very scarce. Particle swarm optimization (PSO) is an effective method for multi-objective problems, however, it has not been well scaled...
Odor source localization is very important in real-world applications. We studied the problem of odor source localization and presented a modified particle swarm optimization algorithm for odor source localization of multi-robot. The algorithm dynamically adjusts two learning factors in the velocity update equation based on the effect of wind on self-cognition and social cognition of a particle. In...
An evolutionary mechanism of local-competing and global-cooperating is presented for cooperative parallel mechanism based multi-particle-swarm optimizer (CP-MPSO), the competitive relationship between the particles of the traditional serial particle swarm optimizer is analyzed. A weighted-best-information based the PSO with cooperation-characteristic is proposed. Finally, the implementation of CP-MPSO...
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