M.tech thesis on particle swarm optimization

Our research is divided into three main components: 1 a proposal which extends PSO to handle multiple objectives.

A Brief Introduction of Particle Swarm Optimization

The main novelty of the approach consists on using a clustering technique in order to divide the population of par- ticles into several subswarms in variable space. Such modification, significantly improves the quality of the Pareto fronts produced, since in each subswarm emerge a local search be- havior.

Also, in order to reduce the non-dominated set, we propose an additional approach to decide whether a solution is accepted or not. Our proposal uses a simple criterion based on closeness of a particle to the feasible region in order to select a leader.

Our comparison of results indicates that the proposed approach is highly competitive with respect to three constraint-handling tech- niques representative of the state-of-the-art in the area. This constraint-handling approach was implemented into our multiobjective particle swarm optimization algorithm MOPSO. The results indicate that our approach is a viable alternative since it outperformed some of the best multiobjective evolutionary algorithms known to date.

ul-ai.kz/includes/2019-06-19/1275-goroskop-sobaka-deva.php MTech thesis. The economic load dispatch plays an important role in the operation of power system, and several models by using different techniques have been used to solve these problems.


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Several traditional approaches, like lambda-iteration and gradient method are utilized to find out the optimal solution of non-linear problem. More recently, the soft computing techniques have received more attention and were used in a number of successful and practical applications. The purpose of this work is to find out the advantages of application of the evolutionary computing technique and Particle Swarm Optimization PSO in particular to the economic load dispatch problem.


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Here, an attempt has been made to find out the minimum cost by using PSO using the data of three and six generating units. In this work, data has been taken from the published work in which loss coefficients are also given with the max-min power limit and cost function.

PSO is applied to find out the minimum cost for different power demand which is finally compared with both lambda- iteration method and GA technique.

A NICHING PARTICLE SWARM OPTIMIZER , A.P.