Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Black-box optimization benchmarking for noiseless function testbed using particle swarm optimization
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Locust swarms: a new multi-optima search technique
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Selection strategies for initial positions and initial velocities in multi-optima particle swarms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Each particle in a swarm maintains its current position and its personal best position. It is useful to think of these personal best positions as a population of attractors --- updates to current positions are based on attractions to these personal best positions. If the population of attractors has high diversity, it will encourage a broad exploration of the search space with particles being drawn in many different directions. However, the population of attractors can converge quickly --- attractors can draw other particles towards them, and these particles can update their own personal bests to be near the first attractor. This convergence of attractors can be reduced by having a particle update the attractor it has approached rather than its own attractor/personal best. This simple change to the update procedure in particle swarm optimization incurs minimal computational cost, and it can lead to large performance improvements in multi-modal search spaces.