ACCELERATING COMPONENTS INFORMATION BASED PARTICLE SWARM OPTIMIZATION WITH TIME VARYING ACCELERATION COEFFICIENTS

Abstract

Time to time, many researchers have suggested modifications to the standard parti cle swarm optimization to find good solutions faster than the evolutionary algorithms. However, it may fall into local optimum (premature convergence) when solving complex multimodal problems. To overcoming such problems, in this thesis accelerating com ponents information based particle swarm optimization with time varying acceleration coefficients algorithm is proposed. In the proposed algorithm, new combination compo nent was introduced based on the average of all individual best position and global best position. Such mechanism can help the algorithm to escape local optimum by combining the learning experience of particle itself and other particles experiences. The global best position plays an important role in improving the convergence rate but reduce the diver sity of population. If global optimum is not close to the best particle the particles may trap in local minima. At the same time the combination component information can lead the particles exploring to a better location by balancing their positions and weaken the attraction of the best particle. Therefore, every swarm updates its position based upon cognitive, social and combination component information and to proper control of these three components during the initial and latter part of the search, time varying acceleration coefficients are employed. The proposed algorithm is coded in MATLAB R2019a and twenty four benchmark test functions which have different characteristics are optimized. In terms of quality solution, convergence speed and robustness, the exper imental results show that the proposed algorithm has a superior performance compared to previously reported results of other three particle swarm optimization algorithms. This suggests that the proposed algorithm has the ability to escape local optima solu tions and achieving the global optimum efficiently.