Hybrid Weight Strategy for Particle Swarm Optimization

Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-based in solving modern time problems. Various improvements have been proposed this terms of internal computation, acceleration coefficients, stopping criteria, hybridization, velocity upgradation etc. The objective paper implement hybrid weights and, therefore, improve the quality PSO algorithm. In case weights, we combined two at a time. These are mixed various but not equal proportions and tested against ten standard testing functions along with pre-existing weights. By using collection, analysed them on three parameters-mean, deviation, minimum value achieved. Later on, after analysing data, out that overall better option respect