An accelerated CLPSO algorithm
This work provides an incremental improvement for researchers and practitioners needing faster optimization algorithms in real-time scenarios.
The paper tackled the problem of high computational complexity in the comprehensive learning particle swarm optimization (CLPSO) algorithm, achieving a reduction in complexity with only a slight performance loss, enabling its use in time-critical applications like real-time tracking and equalization.
The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.