Optimized Algorithm for Particle Swarm Optimization

Particle swarm optimization (PSO) is becoming one of
the most important swarm intelligent paradigms for solving global
optimization problems. Although some progress has been made to
improve PSO algorithms over the last two decades, additional work
is still needed to balance parameters to achieve better numerical
properties of accuracy, efficiency, and stability. In the optimal
PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor,
respectively. By the same token, the same optimal weightings have
been applied for intensification searches and diversification searches,
respectively. Perturbation and constriction effects are optimally
balanced. Simulations of the de Jong, the Rosenbrock, and the
Griewank functions show that the optimal PSO algorithm indeed
achieves better numerical properties and outperforms the canonical
PSO algorithm.

Authors:

• Fuzhang Zhao

Keywords:

• Diversification search
• intensification search
• optimal weighting
• particle swarm optimization.

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