A hybrid multi-agent based particle swarm optimization for telemedicine system for neurological disease

Author(s):  
Arshbeer Kaur ◽  
Sikander S. Cheema
2011 ◽  
Vol 110-116 ◽  
pp. 5206-5210
Author(s):  
K.Ravi Kumar ◽  
S. Anand ◽  
M. Sydulu

This paper proposes a comparison of new evolutionary multi agent based particle swarm optimization algorithms for solving optimal power flows with security constraints (line flows and bus voltages). These methods combine the multi agents in two dimensional and cubic lattice structures with particle swarm optimization (PSO) to form two new algorithms. In both Two Dimensional Lattice Structured Multi Agent based Particle Swarm Optimization (TDLSMAPSO) and Cubic Lattice Structured Multi Agent based Particle Swarm Optimization (CLSMAPSO), an agent represents a particle in cubic lattice structure to PSO, and a candidate solution to the OPF problem. All agents live in a square and cubic lattice like environments, with agents fixed on a lattice point in the ascending order of their fitness value. In order to obtain the optimal solution, each agent in cubic and square lattice competes and cooperates with its neighbors. Making use of these agent-agent interactions, CLSMAPSO and TDLSMAPSO realizes the purpose of minimizing the objective function value. CLSMAPSO and TDLSMAPSO realizations were applied to IEEE 30 bus system. Simulation results show that proposed approaches gives better solution than earlier reported approaches in quick time.


2020 ◽  
Vol 30 (1) ◽  
pp. 413-428
Author(s):  
Nachamada Vachaku Blamah ◽  
Aderemi Adewumi Oluyinka ◽  
Gregory Wajiga ◽  
Yusuf Benson Baha

Abstract This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied problem.


Sign in / Sign up

Export Citation Format

Share Document