Abstract:
The particle swarm optimization algorithm has been successfully shown to perform well on
various optimization problems. Nevertheless, it may be easy to get trapped in a local optimum
(premature convergence) when solving complex multimodal problems. Improving performance
on complex multimodal problems by increasing convergence speed and avoiding the local
optima (premature convergence) have become the two most important and appealing goals in
particle swarm optimization research. To achieve both goals, in this thesis, a linear combination
component information-based particle swarm optimization algorithm is proposed. In the
proposed algorithm, linear combination component information is investigated and added to the
position update equation in order to diversify the movement of particles and avoid premature
convergence. Therefore, every swarm updates its position based on cognitive and social
components, which are considered linear combination information, and the key aspect used here
is that these parameters are no longer assumed to be accelerating components but rather
position components. The proposed algorithm is coded in MATLAB R2019a, and twenty
benchmark test functions that have different characteristics are optimized. In terms of quality
solution and convergence speed, the experimental results show that the proposed algorithm has
superior performance compared to previously reported results of cognitive and social
information-based particle swarm optimization algorithms. This suggests that the proposed
algorithm has the ability to escape local optimal solutions and achieve the global optimum
efficiently
