  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.
