A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm

Sahnehsaraei, Morteza Andalib; Mahmoodabadi, Mohammad Javad; Taherkhorsandi, Milad; Castillo-Villar, Krystel K.; Yazdi, S. M. Mortazavi

doi:10.1007/978-3-319-12883-2_2

Cited by 13 publications

(8 citation statements)

References 63 publications

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“…As expressed in [51][52][53][54][55] and the Global Optimization Toolbox in Matlab platform, advantages and disadvantages of common global optimization algorithms are shown in Table 1. Almost all the methods have flaws in actual application.…”

Section: Economic Dispatchmentioning

confidence: 99%

A Review on Multi-Agent Technology in Micro-Grid Control

Ding

Yin

2018

Electron Sci Technol Appl

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Micro-Grid (MG) integrates renewable generation, storage devices and controllable generations, it provides efficent utilization of clean energy while keeping stable external characteristics. Capability of continuous power supply, high scalability and flexible operation modes can satifiy the current demand of joint operation of renewable generation and Macro-Grid, and will provide a solid foundation for smart grid technology in the future. Thus, MG is an excellent integration of renewable energy utilization with a bright future, Multi-Agent System (MAS) is a new hierarchical control platform and can completely cover all the devices within a MG, its flexible control modes meet the needs of various operations of MG, and the capability of distributed computing supports intelligent functions of MG in the future. Therefore, developing premium functions for MAS in MG control will promote the development of both MG and Smart Grid technologies. This paper reviews the current applications of MAS technology for MG both in basic and advanced control demands. For basic demands concerning safe operations for MG, functions of MAS are available, but a further improvement of performance is essential for future researches to increase penetration of MAS in MG control; For advanced demands, MAS should increase calculation speed to meet the complex need of MG. In the last part, the future focuses are also depicted.

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Section: Economic Dispatchmentioning

confidence: 99%

A Review on Multi-Agent Technology in Micro-Grid Control

Ding

Yin

2018

Electron Sci Technol Appl

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“…HS and FA. In [15] PSO is hybridized with Genetic Algorithm (GA) in order to utilize the unique advantages of both the algorithms. The hybridized algorithm efficiently uses the operations of PSO and GA such as single or multiple crossover, mutation, and the PSO formula.…”

Section: Related Workmentioning

confidence: 99%

“…In the past, various hybrid optimization algorithms have been developed which demonstrate efficient results [13][14][15][16][17][18][19][20][21][22][23]. So, based on the description of BOA and ABC in the previous sections, the two approaches are combined and a hybrid BOA/ABC algorithm is proposed.…”

Section: The Proposed Hybrid Boa/abc Algorithmmentioning

confidence: 99%

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Arora¹,

Singh²

2017

IJIMAI

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-In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC.

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“…Many methods of EAs have been suggested to deal with such problems [13,14]. This optimization problem has also been considered by different heuristic methods such as; tabu search [15][16][17], simulated annealing [18][19][20], memetic algorithms [11], differential evolution [21,22], particle swarm optimization [23,24], ant colony optimization [25], variable neighborhood search [26], scatter search [27,28] and hybrid approaches [29][30][31]. Multiple applications in various areas such as computer engineering, computer science, economic, engineering, computational science and medicine can be expressed or redefined as problem in Equation (1), see [2,32] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

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A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm

Cited by 13 publications

References 63 publications

A Review on Multi-Agent Technology in Micro-Grid Control

A Review on Multi-Agent Technology in Micro-Grid Control

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

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