Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

Dubey, Hari Mohan; Pandit, Manjaree; Panigrahi, B. K.

doi:10.1016/j.renene.2016.06.039

Cited by 65 publications

(17 citation statements)

References 48 publications

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“…Likewise, GWO mimics how a population of wolves chase a prey, inspired in its social hierarchy. Both have been evaluated for different applications, such as power systems control [24][25][26][27], process planning [28], and data gathering [29], as well as their multi-objective approaches [30,31]. This fact corroborates their application potential.…”

Section: Introductionmentioning

confidence: 79%

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Oliveira

Boaventura‐Cunha

et al. 2020

Robotics

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The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

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Section: Introductionmentioning

confidence: 79%

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Oliveira

Boaventura‐Cunha

et al. 2020

Robotics

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“…Examples include the rational design of dielectric nanoantennas, 10 and plasmonic waveguides, 11 the optimization of Stirling heat pumps, 12 the design of thermal-energy storage systems, [13][14][15] and optimizations on scheduling problems in combined hydro-thermo-wind power plants. 16 In the aforementioned applications, analytic models assessed the merit of a set of parameters by evaluating the objectives with fast computations. As such, these optimization problems could be approached with methods identifying the entire set of solutions which cannot be further optimized in at least one of the objectives, at the expense of numerous evaluations of proposed parameter sets.…”

Section: Introductionmentioning

confidence: 99%

Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

Häse¹,

Roch²,

Aspuru‐Guzik³

2018

Preprint

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<div><div>We introduce Chimera, a general purpose achievement scalarizing function (ASF) for multi-objective optimization problems in experiment design. Chimera combines concepts of a priori scalarizing with ideas from lexicographic approaches. It constructs a single merit-based function which implicitly accounts for a provided hierarchy in the objectives. The performance of the suggested ASF is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the performance and applicability of Chimera on two practical applications: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a system for efficient excitation energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find solutions. The presented applications highlight the interpretability of Chimera to corroborate design choices on tailoring system parameters. Additionally, Chimera appears to be applicable to any set of n unknown objective functions, and more importantly does not require detailed knowledge about these objectives. We recommend the use of Chimera in combination with a variety of optimization algorithms for an efficient and robust optimization of multi-objective problems.</div></div><div><br></div>

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“…The environmental concerns due to the emission of hazardous pollutants from the fossil fuel fired thermal plants force us to consider minimization of emission as another objective. Many strategies like installation of air filters, pollutant‐cleaning equipment, switching to low emission fuels, replacement of aged equipment and generating units, integration of renewable energy sources, and environmental dispatching have taken into considerations . All of them except environmental dispatching need considerable amount of capital and huge replacement/modification/upgrade strategy that can be considered as a long‐term solution.…”

Section: Introductionmentioning

confidence: 99%

“…Many strategies like installation of air filters, pollutant-cleaning equipment, switching to low emission fuels, replacement of aged equipment and generating units, integration of renewable energy sources, and environmental dispatching have taken into considerations. [4][5][6][7][8] All of them except environmental dispatching need considerable amount of capital and huge replacement/modification/ upgrade strategy that can be considered as a long-term solution. Thus, environmental dispatching is an attractive short-term solution to reduce the emission of pollutants, which can be considered with economic load dispatch problem.…”

Section: Introductionmentioning

confidence: 99%

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

Mahdi

Vasant

Abdullah-Al-Wadud

et al. 2017

Int Trans Electr Energ Syst

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Summary In electrical power dispatch problem, economic dispatch (ED) and environmental dispatch problems play a crucial part. Economic dispatch problem refers to the minimization of generation cost, where environmental dispatch problem refers to the minimization of emission of pollutants like CO2, SO2, and NOx from the power generation system. A quantum‐inspired particle swarm optimization (QPSO) technique is presented in this paper to solve many‐objective environmental economic dispatch (EED) problems. Emissions of CO2, SO2, and NOx are considered 3 different objectives, thus making it a 4‐objective problem considering ED. Many‐objective EED problems are defined by using a cubic criterion function, and a max/max price penalty factor is considered to convert all the objectives into a single objective to compare the final results with other well‐known methods found in the literature like Lagrangian relaxation, particle swarm optimization, simulated annealing, and quantum‐behaved bat algorithm. Quantum‐inspired particle swarm optimization is implemented on a 6‐unit system to solve many‐objective EED problems, and at the same time, to show the effectiveness of QPSO in large systems, it is also implemented in a 26‐unit power generation system for ED problem. The obtained results demonstrate and verify the effectiveness and robustness of QPSO to solve many‐objective EED problems. This also shows that QPSO can effectively be implemented in such power dispatch problems.

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Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

Cited by 65 publications

References 48 publications

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

A quantum-inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function

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