LINEAR COMBINATION COMPONENTS INFORMATION BASED PARTICLE SWARM OPTIMIZATION

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