An accelerated CLPSO algorithm

TL;DR

This paper introduces an accelerated version of the comprehensive learning particle swarm optimization (CLPSO) algorithm that reduces computational complexity while maintaining acceptable performance, enabling real-time applications.

Contribution

It presents a novel complexity reduction method for CLPSO, balancing performance and computational efficiency for time-critical tasks.

Findings

Reduced computational complexity of CLPSO

Maintained acceptable optimization performance

Enabled real-time application suitability

Abstract

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.