A hybrid harmony search with arithmetic crossover operation for economic dispatch

Niu, Qun; Zhang, Hongyun; Wang, Xiaohai; Li, Kang; Irwin, G.W.

doi:10.1016/j.ijepes.2014.04.031

Cited by 67 publications

(29 citation statements)

References 71 publications

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“…Another kind of hybridization to improve the performance of a method is to hybridize the method from one category with operators from another method from a different category. Examples of this approach are the hybrid differential evolution with biogeography-based optimization [26], krill herd [27], hybrid harmony search [28], and PSOGSA [29].…”

Section: Introductionmentioning

confidence: 99%

Economic dispatch using hybrid grey wolf optimizer

Jayabarathi

Raghunathan

Adarsh

et al. 2016

Energy

313

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Section: Introductionmentioning

confidence: 99%

Economic dispatch using hybrid grey wolf optimizer

Jayabarathi

Raghunathan

Adarsh

et al. 2016

Energy

313

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“…Meta-heuristic approaches have consequently been employed to solve these problems. Numerous meta-heuristic algorithms have been used to solve the economic dispatch problem such as particle swarm optimization (PSO) [7], differential evolution (DE) [8], harmony search (HS) [9], biogeography-based optimization (BBO) [10], and imperialist competitive algorithm (ICA) [11], etc.…”

Section: Introductionmentioning

confidence: 99%

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Yang

Niu

et al. 2014

J. Mod. Power Syst. Clean Energy

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111

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Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

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“…Unlike the SPX presented in Tsutsui et al (1999), the SPX exploited in GeDEA-II requires only two parents to form a new child. The performance of the GeDEA-II was tested against other different state-of-the-art MOEAs A novel hybrid harmony search HS (Geem et al 2001) with arithmetic crossover operator (AC) (Amjady and NasiriRad 2009) called ACHS was proposed in Niu et al (2014). The proposed ACHS enhances the performance and applicability of the conventional HS.…”

Section: Introductionmentioning

confidence: 99%

“…In HS, the global best information, which can improve the quality of the new harmony and speed up the convergence rate, is not fully utilized. According to Niu et al (2014), the ACHS overcomes this shortcoming of the HS by using the global best information to update the new generated harmony through crossover operation. Yoon et al (2012) proposed a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators.…”

Section: Introductionmentioning

confidence: 99%

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

Λιαγκούρας

Metaxiotis

2015

Soft Comput

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Evolutionary algorithms for multiobjective problems utilize three types of operations for progressing toward the higher fitness regions of the search space. Each type of operator contributes in a different way toward the achievement of the common goal. The mutation operation is responsible for diversity maintenance, while the selection operation favors the survival of the fittest. In this paper we focus our attention on the crossover operator. The crossover operator by default is responsible for the search effort and as such deserves our special attention. In particular, we propose a two-stage crossover (TSX) operator for more efficient exploration of the search space. The performance of the proposed TSX operator is assessed in comparison with the simulated binary crossover operator with the assistance of three wellknown multiobjective evolutionary algorithms, namely the NSGAII, the SPEA2 and the MOCELL, for the solution of the DTLZ1-7 set of test functions. We also compare the proposed TSX with other popular reproduction operators like the differential evolution and the particle swarm optimization. Finally, we examine the efficacy of the TSX operator in handling problems having five objectives. It is shown with the assistance of the Deb, Thiele, Laumanns and Zitzler set of test functions that the TSX operator can substantially improve the results generated by three popular performance metrics for most of the cases.

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A hybrid harmony search with arithmetic crossover operation for economic dispatch

Cited by 67 publications

References 71 publications

Economic dispatch using hybrid grey wolf optimizer

Economic dispatch using hybrid grey wolf optimizer

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

An experimental analysis of a new two-stage crossover operator for multiobjective optimization

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