An application of grey wolf optimisation for combined heat and power dispatch

Jayakumar, N.; Subramanian, S.; Elanchezhian, E.B.; Ganesan, S.

doi:10.1504/ijetp.2015.069821

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…GWO is a new swarm intelligence method proposed recently by Mirjalili et al [9]. Due to its great exploration capacity, it has been successfully applied to many practical problems, such as load frequency control of interconnected power system [10], combined heat and power dispatch [11], design of castellated beams [12], and the two-stage assembly flow shop scheduling problem [13]. However, the initial population of original GWO is generated randomly, which may lead to the fact that the grey wolves in search space are lacking diversity.…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

Yan

Liu

et al. 2017

Mathematical Problems in Engineering

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In order to develop a new and effective prediction system, the full potential of support vector machine (SVM) was explored by using an improved grey wolf optimization (GWO) strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO) was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students' final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC) curve), sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

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Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

Yan

Liu

et al. 2017

Mathematical Problems in Engineering

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“…Grey Wolf Optimization Algorithm Jayakumar et al [173,174] showed the good performance of the GWO algorithm in obtaining feasible and high quality solutions for the multi-objective dispatch of a CHP. They also modeled the CHPEED problem, considering both static and dynamic load conditions, and applied the GWO for this purpose [175].…”

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confidence: 99%

Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview

Alsagri

Alrobaian

2022

Energies

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Combined heat and power (CHP) plants are known as efficient technologies to reduce environmental emissions, balance energy costs, and increase total energy efficiency. To obtain a more efficient system, various optimization methods have been employed, based on numerical, experimental, parametric, and algorithmic optimization routes. Due to the significance of algorithmic optimization, as a systematic method for optimizing energy systems, this novel review paper is focused on the meta-heuristic optimization algorithms, implemented in CHP energy systems. By considering the applied objective functions, the main sections are divided into single-objective and multi-objective algorithms. In each case, the units’ combination is briefly detailed, the objective functions are introduced, and analyses are conducted. The main aim of this paper is to gather a database for the optimization of CHPs, demonstrate the effect of the applied optimization methods on the objective functions, and finally, introduce the most efficient methods. The most significant feature of this paper is that it covers all types of CHP optimization issues including scheduling, sizing, and designing problems, finding the extent of each optimization issue in the relevant papers in the last decade. Based on the findings, in the single-objective problems the combined heat and power economic dispatch (CHPED) issue as a subcategory of the scheduling problems is introduced as the most paid topic; the designing issue is known as the lowest paid topic. In the multi-objective problems, working on various types of CHP optimization problems has been conducted with an almost similar share. The combined heat and power economic emission dispatch (CHPEED) problem with the most share, and the sizing issue with the lowest share. The CHP designing and sizing optimization issues could be introduced as topics to work on more in the future. Additionally, the numerical results of CHPED and CHPEED problems solved by various algorithms are presented and compared. In this regard, specified test systems are considered.

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