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In this paper we present two hybrid Particle Swarm Optimisers combining the idea of the particle swarm with concepts from Evolutionary Algorithms. The hybrid PSOs combine the traditional velocity and position update rules with the ideas of breeding and subpopulations. Both hybrid models were tested and compared with the standard PSO and standard GA models.… (More)
Particle Swarm Optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-Organized Criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.
Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the LifeCycle model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs),… (More)
This paper proposes Hybrid SFL-Bees Algorithm that combines strengths of Shuffled Frog Leaping Algorithm (SFLA) and Bees Algorithms (BA). While SFLA can find optimal solutions quickly because of directive searching and exchange of information, BA has higher random that make it easily escape local optima to find global solutions. Thus Hybrid SFL-Bees… (More)