Quasi Oppositional Population Based Global Particle Swarm Optimizer With Inertial Weights (QPGPSO-W) for Solving Economic Load Dispatch Problem

Salaria, Umair Ahmad; Menhas, Muhammad Ilyas; Manzoor, Sohaib

doi:10.1109/access.2021.3116066

Cited by 21 publications

(20 citation statements)

References 49 publications

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“…Global particle swarm optimizer with inertial weights (GPSO-ω) is a variant of PSO algorithm, which is a stochastic optimization technique used to solve complex optimization problems. QPGPSO-ω, which is an extension of GPSO-ω, was utilized in [53] for the solution of ED-IEEE standard (3, 6, 13, 15, 40, and 140) units for Korean grid thermal test systems under various constraints. QPGPSO-ω outperformed several other methods in solving the EDP, showcasing superior results.…”

Section: Research Focusmentioning

confidence: 99%

“…where X i is the i th d-dimensional solution at a specific iteration. X 0 denotes the inverse number of this i th solution and X qoi represents the respective opposite population [53].…”

Section: Overview Of Qpgpso-ωmentioning

confidence: 99%

“…Figure 1. Flowchart of the proposed QPGPSO-ω.The QPGPSO-ω methodology is demonstrated through a pseudocode[53] (Algorithm 1).…”

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confidence: 99%

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Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

et al. 2023

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The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.

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Section: Research Focusmentioning

confidence: 99%

“…where X i is the i th d-dimensional solution at a specific iteration. X 0 denotes the inverse number of this i th solution and X qoi represents the respective opposite population [53].…”

Section: Overview Of Qpgpso-ωmentioning

confidence: 99%

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Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

et al. 2023

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“…Particle swarm optimization (PSO) is an optimization technique that is inspired by natural phenomena such as bird flocking and fish schooling [17]. Using the flocking analogy, the PSO algorithm maintains a swarm of individuals known as particles, with each particle representing a potential solution.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

Moyo

Dewa

2022

Ren. Ener. & Sust. Dev.

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The use of computational intelligence (CI) in solar photovoltaic (SPV) systems has been on the rise due to the increasing computational power, advancements in power electronics and the availability of data generation tools. CI techniques play an important role in modelling, sizing, forecasting, optimizing, analysing and predicting the performance and control of SPV systems. Thus, CI techniques have become an essential technology as the energy sector seeks to meet the rapidly increasing demand for clean, cheap, and reliable energy. In this context, this review paper aims to investigate the role of CI techniques in the advancements of SPV systems. The study includes the involvement of CI techniques for parameter identification of solar cells, PV system sizing, maximum power point tracking (MPPT), forecasting, fault detection and diagnosis, inverter control and solar tracking of SPV systems. A performance comparison between CI techniques and conventional methods is also carried out toprove the importance of CI in SPV systems. The findings confirmed the superiority of CI techniques over conventional methods for every application studied and it can be concluded that the continuous improvements and involvement of these techniques can revolutionize the SPV industry and significantly increase the adoption of solar energy.

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“…Particle Swarm Optimization (PSO) is a well-known example of swarm intelligence, introduced by Kennedy and Eberhart in 1995 [13] to solve global optimisation problems. Because of its simplicity and efficiency, it has been described in various engineering fields and has become the most effective method for solving optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Data Classification

Bangyal¹,

Nisar

Soomro

et al. 2022

Applied Sciences

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Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.

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Quasi Oppositional Population Based Global Particle Swarm Optimizer With Inertial Weights (QPGPSO-W) for Solving Economic Load Dispatch Problem

Cited by 21 publications

References 49 publications

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

The role of computational intelligence techniques in the advancements of solar photovoltaic systems for sustainable development: a review

An Improved Particle Swarm Optimization Algorithm for Data Classification

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