Details

Autor(en) / Beteiligte

• Amodeo, Lionel
• Talbi, El-Ghazali
• Yalaoui, Farouk

Titel

Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization

Ist Teil von

• Recent Developments in Metaheuristics, 2018, Vol.62, p.1-15

Ort / Verlag

Switzerland: Springer International Publishing AG

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Particle swarm optimization Particle swarm optimization (PSO) PSO is a stochastic algorithm based population that integrates social interactions of animals in nature. Adaptive Particle swarm optimization (APSO) as an amelioration of the original one, improve the performance of global search and gives better efficiency. The APSO defines four evolutionary states: exploration, exploitation, convergence, and jumping out. According to the state, the inertia weight and acceleration coefficients are controlled. In this paper, we integrate Hidden Markov Model Particle swarm optimization (HMM) Hidden Markov Model in APSO to have a stochastic state classification at each iteration. Furthermore, to tackle the problem of the dynamic environment during iterations, an additional online learning for HMM parameters is integrated into the algorithm using online Expectation-Maximization algorithm. We performed evaluations on ten benchmark functions to test the HMM integration inside APSO. Experimental results show that our proposed scheme outperforms other PSO variants in major cases regarding solution accuracy and specially convergence speed.