A Particle Swarm Algorithm Based on a Multi-Stage Search Strategy

Shen, Yong; Cai, Wangzhen; Kang, Hongwei; Sun, Xingping; Chen, Qingyi; Zhang, Haigang

doi:10.3390/e23091200

Cited by 9 publications

(3 citation statements)

References 28 publications

(32 reference statements)

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“…The particle swarm algorithm originated from the study of bird feeding behavior, in which birds search for food randomly, not knowing where their current position is from the food, the simplest and most effective search strategy is to search the area around the bird that is currently closest to the food. The simplest and most effective search strategy is to search the area around the bird that is currently closest to the food and constantly adjust the state of the particles to find the food and reach the global optimum [49]. The basic principle of the ant colony algorithm is that ants release pheromone in the process of searching for food.…”

Section: Path Planning Unitmentioning

confidence: 99%

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

et al. 2023

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The prerequisite for underwater vehicles to accomplish various underwater tasks is to obtain real-time position information about themselves in the underwater environment using internal or external sensors, which is known as underwater positioning and navigation technology. This paper presents a general review of underwater vehicle positioning and navigation technology and illustrates the importance of underwater terrain-aided navigation technology. It also summarizes its general structure and describes the principles and functions of each component. The paper mainly analyzes various mainstream methods applied to underwater terrain-aided navigation, focuses on terrain suitability analysis and terrain matching algorithms, which play a central role in the analysis. Additionally, the research progress of terrain-aided navigation technology is described, and the research methods of terrain suitability analysis, terrain matching navigation, and the latest technology applications are compared. Some shortcomings of the current underwater terrain-aided navigation technology are presented, and future research directions are foreseen to provide a basis for subsequent research.

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Section: Path Planning Unitmentioning

confidence: 99%

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

et al. 2023

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“…Therefore, each particle position converges to a particular solution for a right path which are all evaluated and the best particle experience (P best ) within the best global one (G best ) [83]. Equations ( 3) and ( 4) are the mathematical representation for the velocity ∆X and position x i of each particle where k 1 and k 2 are the cognition coefficients to accelerate the particles to the suitable paths [84]. The parameter ω is called the inertia weight whereas r 1 and r 2 are arbitrary variables that belong in the range [0, 1] [85].…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

et al. 2021

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This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

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“…The dynamic Clan PSO topology was described by Bastos-Filho et al [ 38 ]. Shen et al [ 39 ] proposed a multi-stage search strategy supplemented by mutual repulsion and attraction among particles. The proposed algorithm increases the entropy of the particle population and leads to a more balanced search process.…”

Section: Introductionmentioning

confidence: 99%

Learning Competitive Swarm Optimization

Borowska¹

2022

Entropy

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Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

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A Particle Swarm Algorithm Based on a Multi-Stage Search Strategy

Cited by 9 publications

References 28 publications

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

Survey of Terrain-Aided Navigation Methods for Underwater Vehicles

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

Learning Competitive Swarm Optimization

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