A hybrid method for optimization (discrete PSO + CLA)

Jafarpour, Behnam; Meybodi, Mohammad Reza; Shiry, Saeed

doi:10.1109/icias.2007.4658347

Cited by 10 publications

(10 citation statements)

References 21 publications

(18 reference statements)

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“…From the family of PSO-LA model [10][11][12]. In [10] an intellectual and social movement algorithm based on learning automata has been proposed, in which one LA controls the behavior of the whole swarm.…”

Section: B Pso Based On Learning Automata Algorithmsmentioning

confidence: 99%

“…PSO, like other stochastic search methods, is highly sensitive to adjustment of affective parameters. Recently a LA based PSO model [9][10][11][12] called PSO-LA has been reported to improve the performance of PSO. In [10] a PSO-LA model is proposed in which the LA is responsible for configuring the behavior of the swarm and also balancing the process of global and local search.…”

Section: Introductionmentioning

confidence: 99%

“…In [10] a PSO-LA model is proposed in which the LA is responsible for configuring the behavior of the swarm and also balancing the process of global and local search. In [11] a Cellular LA (CLA) based discrete PSO is introduced as a solution of premature convergence. To reduce the probability of trapping PSO-LA into local minima, in [12] four modifications on PSO-LA model are proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The formula used in previous PSO-LA models [9][10][11][12] for updating the velocities and trajectories of particles is based on standard PSO [1], [2]. Also this work differ from the existing ones by its dynamic global and local combined particle swarm optimization iterative formula, in which all particles dynamically share the best information of themselves, local particle and global particle.…”

Section: Introductionmentioning

confidence: 99%

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Improving Learning Automata based Particle Swarm: An optimization algorithm

Hasanzadeh

Meybodi

Ghidary

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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Abstract-Numerous variations of Particle SwarmOptimization (PSO) algorithms have been recently developed, with the best aim of escaping from local minima. One of these recent variations is PSO-LA model which employs a Learning Automata (LA) that controls the velocity of the particle. Another variation of PSO enables particles to dynamically search through global and local space. This paper presents a Dynamic Global and Local Combined Particle Swarm Optimization based on a 3-action Learning Automata (DPSOLA). The embedded learning automaton accumulates the information from individuals, local best and global best particles then combines them to navigate the particle through the problem space. The proposed algorithm has been tested on eight benchmark functions with different dimensions. The work is unique from its test bed; evaluations contain large population size (150) and high dimension (150). The results show that, fitness and convergence pace is better than traditional PSO, DGLCPSO and previous PSO based LA algorithms. I.INTRODUCTION One of the common methods for optimization of continuous nonlinear functions is particle swarm optimization (PSO) [1], [2]. This algorithm has two main concepts: artificial life and collective intelligent of brutes. The inertia weight (w) [3][4][5] is one of the PSO parameters to bring about a balance between the exploration and exploitation characteristics of PSO. Cooperative PSO (CPSO) is a variation on standard PSO which used multiple swarms [6]. Recently it is proposed a combined dynamic global and local particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO [7].Learning automaton (LA) [8] is a multi-propose casual tool, which is an expansion model for learning machines. PSO, like other stochastic search methods, is highly sensitive to adjustment of affective parameters. Recently a LA based PSO model [9][10][11][12] called PSO-LA has been reported to improve the performance of PSO. In [10] a PSO-LA model is proposed in which the LA is responsible for configuring the behavior of the swarm and also balancing the process of global and local search. In [11] a Cellular LA (CLA) based discrete PSO is introduced as a solution of premature convergence. To reduce the probability of trapping PSO-LA into local minima, in [12] four modifications on PSO-LA model are

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Section: B Pso Based On Learning Automata Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Learning Automata based Particle Swarm: An optimization algorithm

Hasanzadeh

Meybodi

Ghidary

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

View full text Add to dashboard Cite

show abstract

“…The Bitwise LA which was used in DPSO algorithm estimates the particles position and moves them through the search space. Jafarpour, Meybodi and Shiry [59] enhanced the LA-based DPSO using a CLA neighborhood topology, in which each particle was placed in a cell being affected by its best personal information and best information of neighboring particles. Learning automaton has greatly influenced the way in which particles fly in the search space.…”

Section: -1 Improved Psos Using Learning Automatamentioning

confidence: 99%