AEFA: Artificial electric field algorithm for global optimization

Anita, Anita; Yadav, Anupam

doi:10.1016/j.swevo.2019.03.013

Cited by 250 publications

(83 citation statements)

References 33 publications

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“…AEFA [35] is a meta-heuristic algorithm inspired by Coulomb's law of electrostatic. According to Coulomb's law, the electrostatic force (attractive or repulsive) between two…”

Section: B Artificial Electric Field Algorithm (Aefa)mentioning

confidence: 99%

“…The authors of [35] proposed the Coulomb's constant (given by equation 12) as exponentially decaying function. Initially the constant is set to a rather high value to increase the exploration of the algorithm and the value gets reduced iteration by iteration to facilitate the control over search accuracy of the algorithm.…”

Section: B Artificial Electric Field Algorithm (Aefa)mentioning

confidence: 99%

“…As they are generally better at searching the optimal solution than their underlying algorithms, hence we get motivated to propose a new hybridized method to solve an important real-life problem called FS. To this end, we introduce a hybrid of two meta-heuristic algorithms, namely, the popular harmony search (HS) [34] algorithm and recently proposed artificial electric field algorithm (AEFA) [35]. HS algorithm, an old and popular optimization algorithm, takes motivation from the charm of musical harmony to find a better solution hence converts qualitative improvisation into quantitative optimization process.…”

Section: Introductionmentioning

confidence: 99%

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EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many metaheuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics-Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.

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“…AEFA [35] is a meta-heuristic algorithm inspired by Coulomb's law of electrostatic. According to Coulomb's law, the electrostatic force (attractive or repulsive) between two…”

Section: B Artificial Electric Field Algorithm (Aefa)mentioning

confidence: 99%

Section: B Artificial Electric Field Algorithm (Aefa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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“…Plenty of research work in literature provides a comprehensive survey about various optimization techniques especially those are used in solving power system problems is [18: 21]. AEFA is an intelligent population-based optimization algorithm that enthused its principal of operation from Colom's law of the electrostatic force and Newton's law of motion [20]. AEFA uses the mechanism of charged particles where best agent fitness has the largest charge and attracts all other agents with a specific force and acceleration velocity towards an optimal solution.…”

Section: Optimization Algorithms Diversitymentioning

confidence: 99%

“…Thanks to NEPLAN Programming Library (NPL) which is used to export all system-controlled variables to Optimizer. Then uses a C++ artificial electric field algorithm (AEFA) [20] for Optimal power flow framework with a set of iterations till reaching best performance.…”

Section: B Anticipated Co-simulatormentioning

confidence: 99%

Optimizing Energy Savings and Operation of Active Distribution Networks Utilizing Hybrid Energy Resources and Soft Open Points: Case Study in Sohag, Egypt

2020

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This research develops an overall framework for optimal operation of Hybrid Renewable Energy Resources (HRERs) system that involves the combination of PV/WT/Diesel units. In the proposed framework, a co-simulator tool is proposed to find the optimal scheduling of HRERs parameters. It composes HOMER PRO and NEPALN packages that are managed by C++ based online optimizer. Different scenarios of hybridization of energy resources are considered achieving technical, economic and environmental perspectives using the HOMER PRO package. The best allocation and sizing of Soft Open Points (SOPs) and the sharing of HRERs are obtained via multi-objective Artificial Electric Field Algorithm (AEFA). The simulation results propose a new hybrid grid-connected system to supply a realistic case study in Sohag-Egypt. That proposed HRERs system demonstrated a feasible economic solution at acceptable levels of gases emission and fuel consumption. Besides, the optimal operation of the HRERs system with SOPs potentially achieve significant technical perspectives that enhance the system losses, improve the voltage profile and increase the loading capacities of transmission lines. The technical perspective is handled using NEPLAN software. Furthermore, the simulation studies prove the high flexible planning and operation of distribution system at significant improvements technical, economic and environmental perspectives. INDEX TERMS Active distribution networks, hybrid renewable energy resources, techno-economic analysis, HOMER PRO, NEPLAN, artificial electric field algorithm.

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A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations

Devarapalli

Bhattacharyya

Kumari

2021

Concurrency and Computation

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Optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in‐depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self‐clearing fault conditions. A multi‐objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO‐CS and BMO‐PSO), and harris hawks optimizer.

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AEFA: Artificial electric field algorithm for global optimization

Cited by 250 publications

References 33 publications

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

Optimizing Energy Savings and Operation of Active Distribution Networks Utilizing Hybrid Energy Resources and Soft Open Points: Case Study in Sohag, Egypt

A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations

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