Special lssues

Metaheuristics Optimization for Real-World Applications

Submission Deadline: 19 April 2023 (closed)

Guest Editors

Dr. Marwa M. Eid, Delta University for Science and Technology, Egypt.
Dr. Shady Y. El-Mashad, Benha University, Egypt.
Dr. Abdelhameed Ibrahim, Mansoura University, Egypt.
Dr. Abdelaziz A. Abdelhamid, Ain Shams University, Egypt.
Dr. El-Sayed M. El-kenawy, Delta University for Science and Technology, Egypt.

Summary

Metaheuristic optimization deals with optimization problems using metaheuristic algorithms to solve a single objective, and sometimes, optimal solutions may not exist. Finding an optimal solution or even sub-optimal solutions is not an easy task. Most optimizations problems are highly nonlinear, multimodal, and complex constraints or have features that make them hard to be solved with exact techniques.

 

This special issue covers a wide variety of topics concerning

metaheuristics for real-world optimization problems in different areas: manufacturing, renewable energy, image processing, finance, physics, economics, artificial intelligence, etc.


Keywords

Metaheuristics and machine learning
Hybrid metaheuristics
Swarm Intelligence
Evolutionary Algorithms
Time Series and Forecasting
Parallel metaheuristics
Deep learning
Big Data applications

Published Papers


  • Open Access

    ARTICLE

    Deep Autoencoder-Based Hybrid Network for Building Energy Consumption Forecasting

    Noman Khan, Samee Ullah Khan, Sung Wook Baik
    Computer Systems Science and Engineering, Vol.48, No.1, pp. 153-173, 2024, DOI:10.32604/csse.2023.039407
    (This article belongs to the Special Issue: Metaheuristics Optimization for Real-World Applications)
    Abstract Energy management systems for residential and commercial buildings must use an appropriate and efficient model to predict energy consumption accurately. To deal with the challenges in power management, the short-term Power Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization.… More >

  • Open Access

    ARTICLE

    Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

    Hassan Shokouhandeh, Mehrdad Ahmadi Kamarposhti, William Holderbaum, Ilhami Colak, Phatiphat Thounthong
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 809-822, 2023, DOI:10.32604/csse.2023.035827
    (This article belongs to the Special Issue: Metaheuristics Optimization for Real-World Applications)
    Abstract The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the energy supply cost with the… More >

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