A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints

Jiang, Shanhe; Ji, Zhicheng; Shen, Yanxia

doi:10.1016/j.ijepes.2013.10.006

Cited by 186 publications

(78 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The third is the hybrid method in which two or more optimization techniques are combined, including hybrid genetic algorithm (HGA) [37], chaotic PSO with sequential quadratic programming (CPSO-SQP) [38], hybrid PSO and gravitational search algorithm (HPSO-GSA) [39], hybrid differential evolution algorithm based on PSO (DEPSO) [40], hybrid differential evolution with biogeography-based optimization (DE/BBO) [41], hybrid chemical reaction optimization with differential evolution (HCRO-DE) [42], and hybrid imperialist competitive-sequential quadratic programming (HIC-SQP) [43].…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

2018

Complexity

View full text Add to dashboard Cite

Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeographybased learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeographybased learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

2018

Complexity

View full text Add to dashboard Cite

show abstract

“…However, this physical position does not hold good for the varying load. Therefore, it is clear that for varying load, the physical host varies, which poses the need to determine the optimum location [18,19]. In this paper, a method named MEGSA-VMM is proposed, which is a gravitational search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

Polepally¹,

Chatrapati²

2017

IJIES

View full text Add to dashboard Cite

Cloud computing is a trending topic in the field of science and technology since the internet dependent services have been growing rapidly. In this environment, there are a lot of immense infrastructures and resources to satisfy the internet users. When a large number of service requests reach at a particular time, load balancing becomes a necessity. Load balancing involves the effective migration of the resources from the loaded physical machine to the other physical machine. For the effective migration, a method named Modified Exponential Gravitational Search Algorithm based on Virtual Machine Migration strategy (MEGSA-VMM) has been proposed that uses the gravitational concepts for performing the frequency-based velocity computations. MEGSA algorithm is the integration of the gravitational search algorithm and exponential weighted moving average theory. Also, the qualityof-Service (QoS) constraints considered for VM migration are migration cost, migration time, resource usage and energy. Simulation of the proposed method and the comparison of the results obtained, with the traditional methods like Ant-Colony Optimization (ACO), Gravitational Search Algorithm (GSA) and Exponential Gravitational Search Algorithm (EGSA) is performed. The proposed method is found to achieve an optimum migration with a minimum energy at a rate of 0.26 and minimum migration cost at a rate of 0.015.

show abstract

“…Exploration is a very effective way to explore the entire problem space and exploitation is able to make the problem converge to an optimal solution. The most popular metaheuristic algorithms are Genetic Algorithm (GA) [4,[6][7][8], Particle Swarm Optimization (PSO) [9][10][11][12][13][14][15][16][17][18][19][20], Artificial Bee Colony (ABC) Algorithm [21], Fruit Fly Optimization Algorithm (FOA) [22], and the Gravitational Search Algorithm (GSA) [23][24][25] and so on. They have attracted the attention of more and more scholars and can effectively solve many scientific problems, such as classification and prediction.…”

Section: Introductionmentioning

confidence: 99%

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Xia-xia

Bai

2017

Advances in Mathematical Physics

View full text Add to dashboard Cite

Gravitational Search Algorithm (GSA) is a widely used metaheuristic algorithm. Although fewer parameters in GSA were adjusted, GSA has a slow convergence rate. In this paper, we change the constant acceleration coefficients to be the exponential function on the basis of combination of GSA and PSO (PSO-GSA) and propose an improved PSO-GSA algorithm (written as I-PSO-GSA) for solving two kinds of classifications: surface water quality and the moving direction of robots. I-PSO-GSA is employed to optimize weights and biases of backpropagation (BP) neural network. The experimental results show that, being compared with combination of PSO and GSA (PSO-GSA), single PSO, and single GSA for optimizing the parameters of BP neural network, I-PSO-GSA outperforms PSO-GSA, PSO, and GSA and has better classification accuracy for these two actual problems.

show abstract

A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints

Cited by 186 publications

References 46 publications

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Contact Info

Product

Resources

About