Penalty parameter-less constraint handling scheme based evolutionary algorithm solutions to economic dispatch

Manoharan, P. S.; Kannan, P.; Baskar, S.; Iruthayarajan, M. Willjuice

doi:10.1049/iet-gtd:20070423

Cited by 58 publications

(20 citation statements)

References 17 publications

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“…Table 6 shows the results obtained from the proposed SDE in solving non-convex PED problem for the 2700 MW power demand and are compared to other optimizers in literature such as Improved Genetic Algorithm with Multiplier Updating (IGA_MU) [28], Modified Shuffled Frog Leaping Algorithm (MSFLA) [40], Particle Swarm Optimization (PSO) [49], conventional DE [49], Real-coded Genetic Algorithm (RGA) [49], New Particle Swarm Optimization with Local Random Search (NPSO-LRS) [50], Back-tracking Search Algorithm (BSA) [51], Cuckoo Search Algorithm with Cauchy distribution (CSA-Cauchy) [52] and BAT [16]. Table 7 shows the simulation results of SDE for the power demand of 2600 MW and are compared with conventional PSO [49], RGA [49], DE [49], MSFLA [40], Global-best Harmony Search (GHS) [40], BAT [16], SaDE [32]. In Table 8, SDE simulation results have been compared with DE [49], RGA [49], PSO [49] and Adaptive Simulated Annealing (ASA) [53] for 2500 MW load demand.…”

Section: System 2: 10 Machine Multiple Fuel Non-convex Ped (With Valvmentioning

confidence: 99%

“…Table 7 shows the simulation results of SDE for the power demand of 2600 MW and are compared with conventional PSO [49], RGA [49], DE [49], MSFLA [40], Global-best Harmony Search (GHS) [40], BAT [16], SaDE [32]. In Table 8, SDE simulation results have been compared with DE [49], RGA [49], PSO [49] and Adaptive Simulated Annealing (ASA) [53] for 2500 MW load demand. For 2400 MW load demand, SDE outperforms all mentioned algorithms same as all other power demands (2700 MW, 2600 MW and 2500 MW), as illustrated by Table 9.…”

Section: System 2: 10 Machine Multiple Fuel Non-convex Ped (With Valvmentioning

confidence: 99%

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Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

et al. 2018

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This paper presents an optimization method for solving the Power Economic Dispatch (PED) problem of thermal generation units with multiple fuels and valve point loadings. The proposed optimizer is a variant of Differential Evolution (DE) characterized as a Stud Differential Evolution (SDE), which has been proposed earlier and implemented on a hydrothermal energy system. In SDE, an operator named Stud Crossover (SC) is introduced in the conventional DE during the trial vector updating process. In SC operator, a best vector gives its optimal information to all other population members through mating. The proposed algorithm's effectiveness to solve Multiple Fuel PED problem, with and without Valve Point Loading Effects (VPLEs), has been validated by testing it on 10 machine multiple fuel standard test systems having 2400 MW, 2500 MW, 2600 MW, and 2700 MW load demands. The results depict the strength of SDE over various other methods in the literature.

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Section: System 2: 10 Machine Multiple Fuel Non-convex Ped (With Valvmentioning

confidence: 99%

Section: System 2: 10 Machine Multiple Fuel Non-convex Ped (With Valvmentioning

confidence: 99%

Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

et al. 2018

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“…The method adaptively updates different penalty parameter for each constraint. Some extensions and applications of penalty parameter less approach can be found in Liao (2010), Manoharan et al (2008), Jadaan et al (2009) and Jan and Khanum (2012).…”

Section: Related Studiesmentioning

confidence: 99%

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

Datta

Deb

2015

Soft Comput

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The holy grail of constrained optimization is the development of an efficient, scale invariant and generic constraint handling procedure. To address these, the present paper proposes a unified approach of constraint handling, which is capable of handling all inequality, equality and hybrid constraints in a coherent manner. The proposed method also automatically resolves the issue of constraint scaling which is critical in real world and engineering optimization problems. The proposed unified approach converts the single-objective constrained optimization problem into a multi-objective problem. Evolutionary multi-objective optimization is used to solve the modified bi-objective problem and to estimate the penalty parameter automatically. The constrained optimum is further improved using classical optimization. The efficiency of the proposed method is validated on a set of well-studied constrained test problems and compared against without using normalization technique to show the necessity of normalization. The results establish the importance of scaling , especially in constrained optimization and call for further investigation into its use in constrained optimization research.

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“…Therefore, the objective function F T is really composed of a set of nonsmooth and nonconvex fuel cost functions F i ðP i Þ, which each one integrates the valve loading effects and multi-fuel options (Manoharan et al, 2008;Selvakumar & Thanushkodi, 2007). Such ED problem is a nonlinear, nonconvex and nonsmooth optimization problem with multiple minima, which is hard, if not impossible, to solve using traditionally deterministic optimization algorithms.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Moreover, a number of heuristic techniques such as Taguchi method (TM) , Hopfield neural network (HNN) (Park, Kim, Eom, & Lee, 1993), adaptive Hopfield neural network (AHNN) (Lee, Sode-Yome, & Park, 1998), and evolutionary programming (EP) (Sadasivam & Sadasivam, 2000) have been applied to solve ED problem with the consideration of multiple fuel source options. Recently, a few modern approaches such as improved genetic algorithm with multiplier updating (IGA-MU) (Chiang, 2005), new particle swarm optimization (NPSO) with local random search (NPSO-LRS) (Selvakumar & Thanushkodi, 2007), anti-predatory particle swarm optimization (APSO) (Selvakumar & Thanushkodi, 2008), and various evolutionary algorithms (EA) (Manoharan, Kannan, Baskar, & Iruthayarajan, 2008) proposed to solve ED considering both valve loading effects and multi-fuel options together.…”

Section: Introductionmentioning

confidence: 99%

Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm

Amjady

Nasiri-Rad

2010

Expert Systems with Applications

110

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Penalty parameter-less constraint handling scheme based evolutionary algorithm solutions to economic dispatch

Cited by 58 publications

References 17 publications

Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

Multiple Fuel Machines Power Economic Dispatch Using Stud Differential Evolution

Uniform adaptive scaling of equality and inequality constraints within hybrid evolutionary-cum-classical optimization

Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm

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