Memory-based approaches for eliminating premature convergence in particle swarm optimization

Chaitanya, K.; Somayajulu, Durvasula V. L. N.; Krishna, P. Radha

doi:10.1007/s10489-020-02045-z

Cited by 25 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This problem occurs due to the lack of population diversity especially in complex multimodal functions [40]. The work in [37,71,284] presented important PSO variants that have shown remarkable performance in terms of avoiding premature convergence. Nevertheless, much more research is needed to address this problem.…”

Section: A Premature Convergencementioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: A Premature Convergencementioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Advanced MPPTs such as particle swarm optimization (PSO) and JAYA outperform but are not well-suited to addressing large and complex optimization challenges and are prone to triggering at local optima [19,20]. PSO can converge prematurely to sub-optimal solutions [21], while JAYA's sensitivity to initial conditions and limited exploration of the search space can hamper its effectiveness [22]. The large search space inherent in the genetic algorithm (GA) parameter optimization process can significantly hamper the system speed and increase the complexity [23].…”

Section: Introductionmentioning

confidence: 99%

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

Jlidi,

Barambones,

Hamidi

et al. 2024

Energies

View full text Add to dashboard Cite

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever.

show abstract

“…In this work, MOPSO algorithm is chosen for handling our problem. Particle swarm optimization is known for fast convergence, robust adaptability and relative simplicity for implementation [10] [23,24].…”

Section: Introductionmentioning

confidence: 99%

Improved multi objective particle swarm optimization based reactive power optimization for ensuring voltage security of power systems

Hilawie,

Shewarega

2023

Eng. Res. Express

View full text Add to dashboard Cite

In this study an improved multi objective particle swarm optimization (IMOPSO) algorithm is proposed for power system reactive power optimization with the objective of ensuring voltage security. The multi objective particle swarm optimization (MOPSO) is improved by introducing an adapted binary crossover (ABX) to the new positions obtained by the basic particle swarm optimization (PSO) algorithm. Additionally, diversity maintenance strategy is added to the algorithm by employing crowding distance (CD) calculation. The developed algorithm is tested and compared with standard MOPSO and non dominated sorting genetic algorithm (NASGA II). The comparison is made based on the degree of closeness to the true pareto front, as measured by the inverted generational distance (IGD), and based on diversity, as measured by the CDs . The test is made using ZDT1, ZDT2, and ZDT3 test functions. The IMOPSO showed improved performance over MOPSO and NASGA II algorithms in terms of convergence to the true pareto front (PF) and in terms of the speed of convergence as well as in maintaining diversity. The algorithm is then implemented to reactive power optimization of IEEE 14 bus test system. For the implementation purpose, the voltage stability and voltage deviation components of voltage security are formulated as a multi objective functions. The implementation has resulted diverse options of optimal settings of reactive power controlling parameters. The optimal settings proved to produce an improved voltage security as measured in terms of voltage deviation and voltage stability.

show abstract

Memory-based approaches for eliminating premature convergence in particle swarm optimization

Cited by 25 publications

References 47 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

Improved multi objective particle swarm optimization based reactive power optimization for ensuring voltage security of power systems

Contact Info

Product

Resources

About