A novel particle swarm optimization algorithm with adaptive inertia weight

Nickabadi, Ahmad; Ebadzadeh, Mohammad Mehdi; Safabakhsh, Reza

doi:10.1016/j.asoc.2011.01.037

Cited by 704 publications

(354 citation statements)

References 14 publications

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“…However, the standard PSO algorithm also has shortcomings such as premature convergence and bad local searching ability similar to other intelligent algorithms [4] [5] [6]. For example, in the optimization of complex problems in high-dimension, the population may have accumulated to a certain point of stagnation without finding the global optimization point, forming premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

Li¹,

Liu²,

Sun³

et al. 2017

JCC

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To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

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Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

Li¹,

Liu²,

Sun³

et al. 2017

JCC

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“…The maximum velocity was limited to 0.2 times the range as it is usual. The new position of each particle is then given by (2), where x i t+1 is the new particle position: Finally the linear decreasing inertia weight [3,4] is used in the typically referred GPSO design that was used in this study. The inertia weight has two control parameters wstart and wend.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Converting PSO dynamics into complex network - Initial study

Pluháček

Janoštík

Šenkeřík

et al. 2016

AIP Conference Proceedings

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Abstract. In this paper it is presented the initial study on the possibility of capturing the inner dynamic of Particle Swarm Optimization algorithm into a complex network structure. Inspired in previous works there are two different approaches for creating the complex network presented in this paper. Visualizations of the networks are presented and commented. The possibilities for future applications of the proposed design are given in detail.

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“…Finally the linear decreasing inertia weight [3,4] is used in the typically referred GPSO design that was used in this study. The inertia weight has two control parameters wstart and wend.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…In recent years there has been a significant development in the area of evolutionary computational techniques (ECTs) such as the PSO algorithm [1][2][3][4]. One of the promising approaches is the implementation of chaotic sequences as Pseudo-random number generators (PRNGs) [5 -11].…”

Section: Introductionmentioning

confidence: 99%

Performance of Multi-chaotic PSO on a shifted benchmark functions set

Pluháček¹,

Šenkeřík²,

Zelinka³

2015

AIP Conference Proceedings

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Abstract. In this paper the performance of Multi-chaotic PSO algorithm is investigated using two shifted benchmark functions. The purpose of shifted benchmark functions is to simulate the time-variant real-world problems. The results of chaotic PSO are compared with canonical version of the algorithm. It is concluded that using the multi-chaotic approach can lead to better results in optimization of shifted functions.

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A novel particle swarm optimization algorithm with adaptive inertia weight

Cited by 704 publications

References 14 publications

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

Converting PSO dynamics into complex network - Initial study

Performance of Multi-chaotic PSO on a shifted benchmark functions set

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