Applications of Hybrid Particle Swarm Optimization Algorithm: A Survey

Bhatia, Nishita; Chauhan, Piyush; Yadav, Hitesh

doi:10.1007/978-981-15-9689-6_32

Cited by 1 publication

(2 citation statements)

References 30 publications

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“…Here, we discussed the application of hybrid PSO algorithms for the parameter optimization of PV cells. Hybrid PSO algorithms are also applicable for various other applications: maritime transportation [74], space trusses [75], Internet of Things [76], managing deliveries of pharmaceuticals [77], load dispatching [78], power quality improvement [79], economic emission dispatch problems [80], pressurized water reactors [81], the partial shading of PV systems [82], the stability of security systems [83], the control of hybrid energy storage systems [84], the operation of integrated energy systems [85], the control optimization of an inverted pendulum [86], optimal chiller loading [87], and many more [73,[88][89][90][91][92][93].…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the fundamental PSO regularly experiences untimely convergence, and demonstrates poor performance for many intricate and multimodal optimization problems [42,49,57]. Several studies were conducted to improve the performance of basic PSO [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73]. These improvements were largely focused on the population structure and the estimation process of the next velocity of every other particle.…”

Section: Hybrid Pso Algorithmsmentioning

confidence: 99%

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An Investigation on Hybrid Particle Swarm Optimization Algorithms for Parameter Optimization of PV Cells

et al. 2022

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The demands for renewable energy generation are progressively expanding because of environmental safety concerns. Renewable energy is power generated from sources that are constantly replenished. Solar energy is an important renewable energy source and clean energy initiative. Photovoltaic (PV) cells or modules are employed to harvest solar energy, but the accurate modeling of PV cells is confounded by nonlinearity, the presence of huge obscure model parameters, and the nonattendance of a novel strategy. The efficient modeling of PV cells and accurate parameter estimation is becoming more significant for the scientific community. Metaheuristic algorithms are successfully applied for the parameter valuation of PV systems. Particle swarm optimization (PSO) is a metaheuristic algorithm inspired by animal behavior. PSO and derivative algorithms are efficient methods to tackle different optimization issues. Hybrid PSO algorithms were developed to improve the performance of basic ones. This review presents a comprehensive investigation of hybrid PSO algorithms for the parameter assessment of PV cells. This paper presents how much work is conducted in this field, and how much work can additionally be performed to improve this strategy and create more ideal arrangements of an issue. Algorithms are compared on the basis of the used objective function, type of diode model, irradiation conditions, and types of panels. More importantly, the qualitative analysis of algorithms is performed on the basis of computational time, computational complexity, convergence rate, search technique, merits, and demerits.

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