Enhancing Particle Swarm Optimization with a Jumping Strategy
Particle Swarm Optimization (PSO) is a metaheuristic evolutionary computation technique inspired by the social behavior of birds and fish flock. The classical PSO has limitations of slow convergence rate and trapping in local minima, as the dimensions of the data increase. Moreover, the majority of developments in optimization techniques have focused on the accuracy of the solution and have overlooked the convergence speed of the algorithm. Keeping in view the need of an optimization algorithm with fast convergence speed, suitable for high dimensional data space, we have developed a novel optimization method based on the concept of Multi-Cluster Jumping PSO. In the proposed method, the particles in the swarm are divided in different clusters to search for the global optimum solution. Each cluster in the swarm has their own cluster best position which is the best position within a cluster and the global best position is located by clusters communication. In order to avoid trapping in the local optima, a jumping strategy is incorporated for stuck particles through relocation of particles to a random new position. Instead of a completely random initialization of the particles, a semi-random initialization is opted by dividing the entire search space and distribution of particles over the search space in independent slots. The proposed approach has the ability to overcome the limitations of classical evolutionary computation methods and is suitable for optimization of high dimensional dynamic data.
PostDoc: Dr. Atiq ur Rehman
Supervisor: Dr. Samir Brahim Belhaouari
Division of Information and Computing Technology
College of Science and Engineering
Hamad Bin Khalifa University
Education City, Qatar Foundation, Doha, Qatar
#PSO
#Optimization
#HBKU
#Particle Swarm Optimization
#MachineLearning