Engineering Applications of Artificial Intelligence
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
Particle swarm optimiser with hybrid multi-parent crossover and discrete recombination
International Journal of Intelligent Information and Database Systems
Particle swarm with self-organized criticality
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Netlist bipartitioning using particle swarm optimisation technique
International Journal of Artificial Intelligence and Soft Computing
A fuzzified systematic adjustment of the robotic Darwinian PSO
Robotics and Autonomous Systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Hi-index | 0.00 |
This paper describes a method for improving the final accuracy and the convergence speed of Particle Swarm Optimization (PSO) by adapting its inertia factor in the velocity updating equation and also by adding a new coefficient to the position updating equation. These modifications do not impose any serious requirements on the basic algorithm in terms of the number of Function Evaluations (FEs). The new algorithm has been shown to be statistically significantly better than four recent variants of PSO on an eight-function test-suite for the following performance matrices: Quality of the final solution, time to find out the solution, frequency of hitting the optima, and scalability.