Determination of kinetic constants for glycerol acetylation by particle swarm optimization algorithm

2021 ◽  
pp. 130408
Author(s):  
L.G. Tonutti ◽  
B.O. Dalla Costa ◽  
H.P. Decolatti ◽  
G. Mendow ◽  
C.A. Querini
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Kinetic Constants ◽  
Swarm Optimization

A REGION DECOMPOSITION-BASED MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION ALGORITHM

2014 ◽  
Vol 28 (08) ◽  
pp. 1459009 ◽  
Author(s):  
LEI CHEN ◽  
HAI-LIN LIU
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Global Attraction ◽  
Multi Objective ◽  
Good Ability ◽  
Objective Space

In this paper, a novel multi-objective particle swarm optimization algorithm based on MOEA/D-M2M decomposition strategy (MOPSO-M2M) is proposed. MOPSO-M2M can decompose the objective space into a number of subregions and then search all the subregions using respective sub-swarms simultaneously. The M2M decomposition strategy has two very desirable properties with regard to MOPSO. First, it facilitates the determination of the global best (gbest) for each sub-swarm. A new global attraction strategy based on M2M decomposition framework is proposed to guide the flight of particles by setting an archive set which is used to store the historical best solutions found by the swarm. When we determine the gbest for each particle, the archive set is decomposed and associated with each sub-swarm. Therefore, every sub-swarm has its own archive subset and the gbest of the particle in a sub-swarm is selected randomly in its archive subset. The new global attraction strategy yields a more reasonable gbest selection mechanism, which can be more effective to guide the particles to the Pareto Front (PF). This strategy can ensure that each sub-swarm searches its own subregion so as to improve the search efficiency. Second, it has a good ability to maintain the diversity of the population which is desirable in multi-objective optimization. Additionally, MOPSO-M2M applies the Tchebycheff approach to determine the personal best position (pbest) and no additional clustering or niching technique is needed in this algorithm. In order to demonstrate the performance of the proposed algorithm, we compare it with two other algorithms: MOPSO and DMS-MO-PSO. The experimental results indicate the validity of this method.

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