Chaotic Inertia Weight in Particle Swarm Optimization

Feng, Yong; Teng, Guifa; Wang, Ai-Xin; Yao, Yong-Mei

doi:10.1109/icicic.2007.209

Cited by 168 publications

(133 citation statements)

References 4 publications

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“…In our experiment three inertia weight strategies are consideredconstant inertia weight, random inertia weight [2], and chaotic inertia weight [5]. The value of ω for constant inertia weight strategies is set to 0.76.…”

Section: Computational Experience 41 Experimental Setupmentioning

confidence: 99%

Particle Swarm Optimization Algorithm for Integer Factorization Problem (IFP)

Choudhury¹,

Neog²

2015

IJCA

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This paper presents particle swarm optimization (PSO) method to find the prime factors of a composite number. Integer factorization is a well known NP hard problem and security of many cryptosystem is based on difficulty of integer factorization. A particle swarm optimization algorithm for integer factorization has been devised and tested on different 100 numbers. It has been found that the PSO method performs with little variability over swarm size.

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Section: Computational Experience 41 Experimental Setupmentioning

confidence: 99%

Particle Swarm Optimization Algorithm for Integer Factorization Problem (IFP)

Choudhury¹,

Neog²

2015

IJCA

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“…A chaotic inertia weight strategy using the benefits of better ability of mountain climbing and escape from a local optimum in the evolutionary process. In [17], the chaotic inertia weight strategy compromises the simple chaotic motions to the linear inertia weight and random inertia weight to produce the perturb inertia weight.…”

Section: Review On Inertia Weight Strategiesmentioning

confidence: 99%

Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight

Wang

Yang

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThe inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.

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“…There are lots of others strategies for variation of inertia weight like Adaptive Inertia Weight [9], Sigmoid Increasing Inertia Weight [10], Chaotic Inertia Weight [11], Oscillating Inertia Weight [12], Global-Local Best Inertia Weight [13], Simulated Annealing Inertia Weight [14], Exponent Decreasing Inertia [15], Natural Exponent Inertia Weight Strategy [16] Fine parametric tuning of evolutionary algorithms is very important aspect to improve accuracy and efficiency. Earlier approaches [6][7][8] were mainly focused on the variation of inertia weight to increase the efficiency of PSO.…”

Section: Related Workmentioning

confidence: 99%

“…Several authors [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] proposed different methods to achieve better accuracy and convergence. However, in this paper, we have proposed a Modified Particle Swarm Optimization (MPSO) Algorithm based on self-adaptive acceleration constants.…”

Section: Introductionmentioning

confidence: 99%

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

Mandal¹

2017

IJMECS

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Abstract-Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.

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Chaotic Inertia Weight in Particle Swarm Optimization

Cited by 168 publications

References 4 publications

Particle Swarm Optimization Algorithm for Integer Factorization Problem (IFP)

Particle Swarm Optimization Algorithm for Integer Factorization Problem (IFP)

Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

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