Quantum-Behaved Particle Swarm Optimization with Immune Operator
In the previous paper, we proposed Quantum-behaved Particle Swarm Optimization (QPSO) that outperforms traditional standard Particle Swarm Optimization (SPSO) in search ability as well as less parameter to control. However, although QPSO is a global convergent search method, the intelligence of simulating the ability of human beings is deficient. In this paper, the immune operator based on the vector distance to calculate the density of antibody is introduced into Quantum-behaved Particle Swarm Optimization. The proposed algorithm incorporates the immune mechanism in life sciences and global search method QPSO to improve the intelligence and performance of the algorithm and restrain the degeneration in the process of optimization effectively. The results of typical optimization functions showed that QPSO with immune operator performs better than SPSO and QPSO without immune operator.
KeywordsParticle Swarm Optimization Particle Swarm Optimization Algorithm Search Ability Standard Particle Swarm Optimization Immune Memory
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