Hybrid Differential Evolution and Gravitational Search Algorithm for Nonconvex Economic Dispatch

Le, Luong Dinh; Ho, Loc D.; Vo, Dieu Ngoc; Vasant, Pandian

doi:10.1007/978-3-319-13356-0_8

Cited by 7 publications

(3 citation statements)

References 25 publications

(29 reference statements)

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“…The robustness of the GSA in comparison with other evolutionary methods was displayed based on the results obtained. A hybrid GSA and Differential Evolution (DE) which performed competitively with other methods was also proposed for solving an economic dispatch problem in [10]. In [11], the authors compared the performance of methods like PSO, GA, DE, EP, and Simulated Annealing (SA) in solving a dynamic economic dispatch problem.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

Ajayi

Heymann

2021

Heliyon

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Dynamic combined economic and emission dispatch is an important task in the power system that examines the optimal allocation of power generation resources that yield the least possible fuel and emission costs. In this study, the Moth Swarm Algorithm has been proposed for solving the combined economic and emission dispatch problem for a 24-hour period. The model has been implemented on a test system made up of a combination of thermal and solar photovoltaic plants, while also considering spinning reserve allocation. The results obtained are presented in comparison with commonly used state-of-the-art methods like Moth Flame Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, and Tunicate Swarm Algorithm. Two test systems have been considered in the model implementation. The first system consists of a combination of six thermal plants and thirteen solar plants whose load demand is the hourly energy demand of an anonymous data centre in South Africa. The second test system is made up of three thermal plants and thirteen solar plants to service its unique hourly load demand. Results showed that the proposed MSA gave preference to solar photovoltaic generation over thermal generation given that environmental impact minimization is a major component of the overall objective function. The proposed MSA also scheduled thermal generators to provide the required spinning reserve capacity since solar energy is intermittent in nature. Overall, analyses show that the proposed MSA outperformed the other methods in finding the best fuel, emission, solar generation, and spinning reserve costs that best serve the energy demand of the data centre and the second test system for the 24-hour period.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

Ajayi

Heymann

2021

Heliyon

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“…But, in the recent years a variety of researches have started studying on it. Some of the applications of the GSA include: data clustering (Hatamlo et al 2011;Dowlatshahi and Nezamabadi-pour 2014), classification (Bahrololoum et al 2014;Saha and Chakraborty 2015), optimization of modular neural networks (Gonzalez et al 2015a, b), parameter optimization of a low amplifier (Shams et al 2015), reusable launch vehicle approach and landing trajectory optimization (Su and Wang 2015), feature selection (Xiang et al 2015;Han et al 2014), facial emotion recognition (Farahani et al 2014), training feedforward neural networks (Do 2015), dual channel speech enhancement (Prajna et al 2014), novconvex economic dispatch (Le et al 2015), web service selection problem (Zibanezhad et al 2011), path planning (Li and Duan 2012), optimization problems (Nezamabadi-pour 2015), unit commitment problem (Ji et al 2014;Yuan et al 2014) and so on.…”

Section: Introductionmentioning

confidence: 99%

DGSA: discrete gravitational search algorithm for solving knapsack problem

Sajedi

Razavi

2016

Oper Res Int J

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The 0-1 knapsack problem is one of the classic NP-hard problems. It is an open issue in discrete optimization problems, which plays an important role in the real applications. Therefore, several algorithms have been developed to solve it. The Gravitational Search Algorithm (GSA) is an optimization algorithm based on the law of gravity and mass interactions. In the GSA, the searcher agents are a collection of masses that interact with each other based on the Newtonian gravity and the laws of motion. In this algorithm the position of the agents can be considered as the solutions. The GSA is a nature-inspired algorithm that is used for finding the optimum value of continuous functions. This paper introduces a Discrete version of the GSA (DGSA) for solving 0-1 knapsack problem. In this regard, we introduce an approach for discretely updating the position of each agent. In addition, a fitness function has been proposed for 0-1 knapsack problem. Our experimental results show the effectiveness of the DGSA in comparison with other similar algorithms in terms of the accuracy and overcoming the defect of local convergence.

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“…In recent decades, a wide variety of metaheuristic optimization methods such as genetic algorithm (GA) [4,5], artificial immune system (AIS) [6,7], particle swarm optimization (PSO) [8][9][10][11][12][13][14][15][16], differential evolution (DE) [17][18][19], gravitational search algorithm (GSA) [20], Tabu Search (TS) [21,22], neural network (NN) [23,24], evolutionary programming (EP) [25], bacterial foraging algorithm (BFA) [26], biogeography-based optimization (BBO) [27], and other population-based optimization algorithms [28][29][30][31][32] have been applied with success in solving the ED problems and been able to obtain better solutions compared to using conventional optimization methods.…”

Section: Introductionmentioning

confidence: 99%

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Yang

2018

Complexity

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Population-based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve economic dispatch (ED) problems. It is a complex and highly nonlinear constrained optimization problem. The proposed approach, double elitist breeding quantum-based particle swarm optimization (DEB-QPSO), makes use of two elitist breeding strategies to promote the diversity of the swarm so as to enhance the global search ability and an improved efficient heuristic handling technique to manage the equality and inequality constraints of ED problems. Investigating on 15-unit, 40-unit, and 140-unit widely used test systems, through performance comparison, the proposed DEB-QPSO algorithm is able to obtain higher-quality solutions efficiently and stably superior than the other the state-of-the-art algorithms.

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Hybrid Differential Evolution and Gravitational Search Algorithm for Nonconvex Economic Dispatch

Cited by 7 publications

References 25 publications

Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

DGSA: discrete gravitational search algorithm for solving knapsack problem

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

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