Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Kabir, Abdulrashid Muhammad; Kamal, Mohsin; Ahmad, Fiaz; Ullah, Zahid; Albogamy, Fahad R.; Hafeez, Ghulam; Mehmood, Faizan

doi:10.3390/su131910609

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Finally, the AFSA hyperparameter tuning process is performed to boost the intrusion detection performance of the CAE model. The AFSA model is a kind of swarm intelligence technique that is simulated from the behaviour of the animals [20]. In this method, the fish's clustering, collision and foraging behaviours are simulated along with its collective support in a fish swarm to realize the optimum global point.…”

Section: Algorithmic Process Of Afsa Based Hyperparameter Tuningmentioning

confidence: 99%

Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

Maray¹,

Alshahrani²,

Alissa³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed to secure the SDN-enabled IoT environment in this scenario. The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder (HSAFS-OCAE) for intrusion detection in the SDN-enabled IoT environment. The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS (HSAFS) technique is exploited at first for feature selection. Next, the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment. Finally, the Artificial Fish Swarm Algorithm (AFSA) is used to fine-tune the hyperparameters. This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work's novelty. The proposed HSAFS-OCAE technique was experimentally validated under different aspects, and 6588 CMC, 2023, vol.74, no.3 the comparative analysis results established the supremacy of the proposed model.

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Section: Algorithmic Process Of Afsa Based Hyperparameter Tuningmentioning

confidence: 99%

Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

Maray¹,

Alshahrani²,

Alissa³

et al. 2023

Computers, Materials &Amp; Continua

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“…The fuel cost function is optimized to obtain the optimum power generation conditions [1]. Overall fuel cost is expressed in the following terms [2]:…”

Section: Economic Dispatch With Thermal Unitsmentioning

confidence: 99%

“…Minimizing the generation cost and reducing carbon emissions can be accomplished by integrating renewable energy sources such as solar power systems [2]. RES is considered environmentally friendly and non-harmful for electricity generation and supply [3].…”

Section: Introductionmentioning

confidence: 99%

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

Fahim,

Silva,

Hussain

et al. 2023

Sustainability

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Fossil fuel is considered to be the primary power generation source. As this source is not that eco- and environmentally friendly, researchers are constantly searching for an alternative source for power generation. Renewable energy has drawn much attention in this regard in recent times. For solving economic load dispatch issues, numerous operational constraints must be considered. Due to the restructuring of the power sector, there is competition between different power systems organizations. Increasing fossil fuel costs drive power-producing utilities to adopt a cost-effective technique for dispatching actual power output. Due to the presence of nonlinearity and non-convexity in the fuel of cost function of generators, the economic load dispatch is often considered a complex optimization problem. Many researchers have been optimizing fuel costs to solve the economic power dispatch problem. This paper offers a critical analysis of ELD that takes into account both traditional and non-traditional energy sources. The review covers a variety of algorithms, including hybrid algorithms for integrating renewable energy sources (RES). The paper also focuses on several restricted optimization techniques and contemporary algorithms including PSO, Jaya, GWO, SMO, TLBO, Rao, MRao-2, and MFO to reduce the fuel cost of generation units using large-scale solar PV. Moreover, this paper provides a comprehensive overview of the current state of economic load dispatch and provides valuable insights for electricity researchers and practitioners. It also discusses future technologies and next steps in the field of ELD, emphasizing the need for more environmentally friendly and cost-effective power generation and distribution solutions. Overall, the paper demonstrates the benefits of renewable energy sources as well as optimization techniques for creating a more sustainable and efficient power generation system.

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“…Hybrid algorithms are designed by combining several features of two or more metaheuristic algorithms or between a metaheuristic algorithm and a traditional one to increase the efficiency of the hybrid. Among the hybrid algorithms used for the EcD problem we mention the following: gaining-sharing knowledge with DE hybrid algorithm [43], hybrid DE algorithm based on PSO [44], hybrid kernel search with PSO [45], hybrid statistical multiswarm PSO-SCA [46], hybrid Harris Hawks optimizer with adaptivehill-climbing optimizer [10], hybrid PSO and GSA [47], hybrid ICA-PSO [48], hybrid artificial algae algorithm [49], comprehensive learning cuckoo search with chaos-lambda method [50], hybrid GWO [51,52], hybrid invasive weed optimization algorithm [53], hybrid genetic-artificial fish swarm algorithm [54], etc.…”

Section: Introductionmentioning

confidence: 99%

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

Secui,

Secui

2024

Appl Intell

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Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals’ evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time.

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Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Cited by 7 publications

References 36 publications

Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

Social small group optimization algorithm for large-scale economic dispatch problem with valve-point effects and multi-fuel sources

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