A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

Pesquisa Operacional

Bruno A. Angélico²  Márcio Mendonça²  Taufik Abrão¹  Lúcia Valéria R. De Arruda¹ 

[1] ,Federal University of TechnologyCornélio Procópio,Brazil

关键词: Fuzzy Cognitive Maps;    Particle Swarm Optimization;    Genetic Algorithm;    process control;    statistical convergence analysis;   

DOI  :  10.1590/S0101-74382013005000009

来源: SciELO

【 摘 要 】

This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM) learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

【 授权许可】

CC BY   
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