Swarm intelligence
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Emotional particle swarm optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
An emotional particle swarm optimization algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Multi-circle detection on images inspired by collective animal behavior
Applied Intelligence
Adaptive cooperative particle swarm optimizer
Applied Intelligence
Cooperative Velocity Updating model based Particle Swarm Optimization
Applied Intelligence
An improved quantum-behaved particle swarm optimization algorithm
Applied Intelligence
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This paper proposes a novel approach to swarm particle optimization based on emotional behavior to solve real optimization problems. In the trend of PSO manipulating self-adaptive control to regulate potential parameters, the proposed algorithm involves both a semi-adaptive inertia weight and an emotional factor at the level of the velocity rule. The semi-inertia weight highlights a specific comportment. Thus, due to the few changes occurred in its adaptive "life", it continues to evolve with a significantly smaller constant for the benefit of a finer exploitation. The emotion factor presents an important feature of convergence because it splits up the search space into potential regions that are finely explored by sub-swarm populations with the same emotions. The principle of particles with multiple emotions intended for the categorization of particles into specific emotional classes. The idea behind this principle is to divide to conquer, and due to presence of multiple emotional classes the multidimensional search space is widely explored at the search of the best position. Emotional PSO is evaluated on the test suit of 25 functions designed for the special session on real optimization of CEC 2005, and its performances are compared to the best algorithm the restart CMA-ES.