A novel particle swarm optimisation with search space tuning parameter to avoid premature convergence.
Raja ChandrasekaranR. Agilesh SaravananD. Ashok KumarN. GangatharanPublished in: Int. J. Math. Model. Numer. Optimisation (2019)
Keyphrases
- particle swarm optimisation
- premature convergence
- global search
- search space
- solution space
- genetic algorithm ga
- parameter settings
- fitness function
- differential evolution
- convergence speed
- pso algorithm
- search capabilities
- particle swarm
- mutation operator
- inertia weight
- search algorithm
- global optimization
- particle swarm optimization
- hybrid algorithm
- evolutionary programming
- simulated annealing
- genetic algorithm
- population diversity
- convergence rate
- particle swarm optimization algorithm
- metaheuristic
- test functions
- exhaustive search
- evolution strategy
- parameter values
- branch and bound
- genetic programming
- neural network
- candidate solutions
- optimal parameters
- optimisation problems
- particle swarm optimization pso
- evolutionary process
- optimal solution
- evolutionary algorithm
- global optimum
- machine learning
- faster convergence
- artificial intelligence
- swarm intelligence