A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Local Parameters Particle Swarm Optimization
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Empirical comparison of MOPSO methods: guide selection and diversity preservation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving differential evolution by altering steps in EC
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Improving differential evolution through a unified approach
Journal of Global Optimization
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Particle swarm optimization (PSO) has been in practice for more than 10 years now and has gained wide popularity in various optimization tasks. In the context to single objective optimization, this paper studies two aspects of PSO: (i) its ability to approach an 'optimal basin', and (ii) to find the optimum with high precision once it enters the region. of interest. We test standard PSO algorithms and discover their inability in handling both aspects efficiently. To address these issues with PSO, we propose an evolutionary algorithm (EA) which is algorithmically similar to PSO, and then borrow different EA-specific operators to enhance the PSO's performance. Our final proposed PSO contains a parent-centric recombination operator instead of usual particle update rule, but maintains PSO's individualistic trait and has a demonstrated performance comparable to a well-known GA (and outperforms the GA in some occasions). Moreover, the modified PSO algorithm is found to scale up to solve as large as 100-variable problems. This study emphasizes the need for similar such studies in establishing an equivalence between various genetic/evolutionary and other bio-inspired algorithms, a process that may lead us to better understand the scope and usefulness of various operators associated with each algorithm.