Fuzzy adaptive particle swarm for single and multi-objective optimization problems

Abstract EN

This paper proposed a fuzzy adaptive particle swarm optimization (FAPSO) for single and multi-objective optimization problems (MOOPs).

This algorithm considers the development a maintain of particle swarm optimization with fuzzy set theory to solve the complexities of non linear programming with high dimensions.

This combination achieved in this algorithm two objectives.

One of them ermines the initial position of particles instead of selecting them randomly and ranking them by using the fuzzy utility function.

Therefore, it is easy to select the and for MOOP.

Another one is controlling the velocity of the particles whereas it is the reason to reveal the premature convergence and the particles divergence cases.

The FAPSO is tested using a set of benchmark problems to illustrate the effectiveness of overcoming the premature convergence and the particles divergence cases.

Also, for finding the multiple pareto optimal solution of MOOPs.