Efficient Population Utilization Strategy for Particle Swarm Optimizer

Hsieh, Sheng-Ta; Sun, Tsung-Ying; Liu, Chan-Cheng; Tsai, Shang-Jeng

doi:10.1109/tsmcb.2008.2006628

Cited by 152 publications

(23 citation statements)

References 14 publications

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“…As for the population size, LPSO (ladder PSO) was developed by Chen and his colleagues (Chen & Zhao, 2009), where the diversity of the swarm was evaluated by Manhattan distance and the size of the swarm was dynamically adjusted accordingly. Hsieh et al proposed an EPUS-PSO (efficient population utilization strategy PSO), where the swarm size was dynamically adjusted via a number of swarm management strategies such as adding/deleting/replacing the swarm particles, thus the learning and sharing of the optimal position can be achieved for the particle swarm (Hsieh et al, 2009). In Zeng et al (2020), a DNSPSO (dynamic-neighbourhoodbased switching PSO) was proposed, where a new velocity updating scheme was designed to regulate the personal best position and the global best position in terms of a distance-based dynamic neighbourhood to make full use of the population evolution information among the entire swarm.…”

Section: Related Work Of Pso Variantsmentioning

confidence: 99%

An improved particle swarm optimization algorithm with adaptive weighted delay velocity

Song

Cao

2021

Systems Science & Control Engineering

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An improved particle swarm optimization (PSO) with adaptive weighted delay velocity (PSO-AWDV) is proposed in this paper. A new scheme blending weighted delay velocity is firstly presented for a new PSO with weighted delay velocity (PSO-WDV) algorithm. Then, to adaptively update the velocity inertia weight, an adaptive PSO-AWDV algorithm is developed based on the evolutionary state of the particle swarm evaluated via a new estimation method. The newly proposed adaptive PSO-AWDV algorithm is tested based on some famous benchmark functions, which can confirm that the performance of PSO-AWDV is superior to several well-known PSO variants and intelligent optimization algorithms in literature.

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Section: Related Work Of Pso Variantsmentioning

confidence: 99%

An improved particle swarm optimization algorithm with adaptive weighted delay velocity

Song

Cao

2021

Systems Science & Control Engineering

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“…In recent research, the PSO algorithm with inertia weight adjusted by the average absolute value of velocity or the situation of swarm is proposed to keep the balance between local search and global search [56][57][58]. In addition, the adaptive population size strategy is an effective way to improve the accuracy and efficiency of the PSO algorithm [59][60][61][62].…”

Section: Adaptive Inertia Weight Strategiesmentioning

confidence: 99%

Effects of Random Values for Particle Swarm Optimization Algorithm

2018

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Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 (U[0, 1], U[−1, 1] and G(0, 1)), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U[−1, 1] or G(0, 1) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U[−1, 1] or G(0, 1) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

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“…Shi and Eberhart (1998) first introduced the opinion of inertia weight by presenting constant inertia weight. The most popular articles on inertia weight strategies are summarized in Table 1, and they are as follows: Shi and Eberhart (1999), Eberhart and Shi (2001), Kennedy and Mendes (2002), Chatterjee and Siarry (2006), Lei et al (2006), Fan and Chiu (2007), Feng et al (2007), Gao et al (2008), Jiao et al (2008), Li and Gao (2009), Hsieh et al (2009), Ting et al (2012), Chauhan et al (2013). In addition, the most cited articles published in the ISI Web of Knowledge were taken into account in this study.…”

Section: Introductionmentioning

confidence: 99%

The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model

Özsoy

2021

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Purpose This paper aims to consider each strategy of the particle swarm optimization (PSO) as a unit in data envelopment analysis (DEA) and uses the minimax mixed-integer linear programming DEA approach to find the most suitable inertia weight strategy. A total of 15 inertia weight strategies were empirically examined in a suite of 42 benchmark problems in the view of DEA. Design/methodology/approach PSO is very sensitive to inertia weight strategies, and therefore, an important amount of research attempts has been concentrated on these strategies. There is no research into the determination of the most suitable inertia weight strategy; however, there are a large number of comparisons related to the inertia weight strategies. DEA is one of the performance evaluation methods, and its models classify the set of strategies into two distinct sets as efficient and inefficient. However, only one of the strategies should be used in the PSO algorithm. Some effective models were proposed to find the most efficient strategy. Findings The experimental studies demonstrate that an approach is a useful tool in the determination of the most suitable strategy. Besides, if the author encounters a new complex problem whose properties are known, it will help the author to choose the best strategy. Practical implications A heavy oil thermal cracking three lumps model for the simplification of the reaction system was used because it is an important complicated chemical process. In addition, the soil water retention curve (SWRC) plays an important role in diverse facets of agricultural engineering. As the SWRC can be regarded as a nonlinear function between the water content and the soil water potential, Van Genuchten model is proposed to describe this function. To determinate these model parameters, an optimization problem is formulated, which minimizes the difference between the measured and modeled data. Originality/value In this paper, the PSO algorithm is integrated with minimax mixed-integer linear programming to find the most suitable inertia weight strategy. In this way, the best strategy could be chosen for a new more complex problem.

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Efficient Population Utilization Strategy for Particle Swarm Optimizer

Cited by 152 publications

References 14 publications

An improved particle swarm optimization algorithm with adaptive weighted delay velocity

An improved particle swarm optimization algorithm with adaptive weighted delay velocity

Effects of Random Values for Particle Swarm Optimization Algorithm

The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model

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