A novel particle swarm optimization based on prey–predator relationship

Qiu, Rui; Yuan, Meng; Liang, Yongtu; Liao, Qi

doi:10.1016/j.asoc.2018.04.008

Cited by 57 publications

(26 citation statements)

References 30 publications

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“…Each particle updates its location through following two optimal values: one is determined by each particle, which is denoted as personal best (abbreviated as

) and the other by the whole population (abbreviated as

). Each particle updates its value when individual fitness value conforms to the comparison expression

[47] .…”

Section: Modeling Steps and Solution Methodsmentioning

confidence: 99%

Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19

Huang¹,

Liao²,

Qiu³

et al. 2021

Applied Energy

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With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models’ prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events.

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“…Each particle updates its location through following two optimal values: one is determined by each particle, which is denoted as personal best (abbreviated as

) and the other by the whole population (abbreviated as

). Each particle updates its value when individual fitness value conforms to the comparison expression

[47] .…”

Section: Modeling Steps and Solution Methodsmentioning

confidence: 99%

Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19

Huang¹,

Liao²,

Qiu³

et al. 2021

Applied Energy

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“…This part critically reviews PSO variants that are published recently. The authors in [77] proposed a new PSO variant named prey-predator PSO (PP-PSO) that implements catch, escape, and breeding strategies that can assist in enhancing the convergence speed and reduce the computational time. The proposed approach is tested on 10 classical benchmarking functions and the CEC2017 test suite for 10, 30, and 100 dimensions.…”

Section: B Recent Pso Variantsmentioning

confidence: 99%

“…Therefore, further work is needed to validate their effectiveness on high-dimensional problems. The PSO variants presented in[77] [81] [84] [85] [96] did not provide any statistical analysis which is essential to show the significance and the superiority of these variants. Finally, the PSO variants in [82] [84] [85] [87] [89] [96] compared their performance with PSO variants without considering other robust and well-known optimization algorithms.…”

mentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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“…PSO is an evolutionary optimization algorithm proposed by Kennedy and Eberhart in the 1990s [84] that uses swarms of particles in its search for the global optimum for a given problem. It was inspired by the social behavior of animals in search of food or prey [28], having as characteristics robustness and efficiency in the search for the global optimum [85]. PSO has been used in many fields of knowledge, such as vehicle routing, multi-objective optimization and control systems [86].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

Franco¹,

Steiner²

2021

JGIS

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The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model's accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.

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A novel particle swarm optimization based on prey–predator relationship

Cited by 57 publications

References 30 publications

Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19

Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19

Particle Swarm Optimization: A Comprehensive Survey

Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

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