Special Issue "Optimization Algorithms for Engineering Applications"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: 31 December 2023 | Viewed by 1028

Special Issue Editors

School of Information Engineering, Sanming University, Sanming 365004, China
Interests: Remora Optimization Algorithm (ROA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals
Prince Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Mafraq 130040, Jordan
Interests: Arithmetic Optimization Algorithm (AOA);, Bio-inspired Computing;, Nature-inspired Computing;, Swarm Intelligence;, Artificial Intelligence;, Meta-heuristic Modeling, and Optimization Algorithms;, Evolutionary Computations;, Information Retrieval;, Text clustering;, Feature Selection;, Combinatorial Problems;, Optimization;, Advanced Machine Learning;, Big data;, and Natural Language Processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Engineering application refers to applying theoretical knowledge, science, technology, scientific research results, experimental results, inventions, etc., through industrialization in the actual engineering of production, manufacturing, and construction processes. Various engineering applications are complex real-world problems. They are generally motivated by the ideas of evolution, human and animal behavior, or MPC (mathematics, physics, and chemistry). Optimization algorithms are widely acknowledged in the application of solving complex problems, such as non-convex, nonlinear constraints, and high-dimensional problems. There is a lot of literature on optimization algorithms, and they all eventually find the optimal solution through an exploration and exploitation process. Due to the stochastic nature of optimization algorithms, accurate and adequate results can be produced at a small cost.

This Special Issue intends to capture recent contributions of high-quality papers focusing on interdisciplinary research on the optimization algorithm for engineering applications using modern computational intelligence theories, approaches, and experiments. We invite the researchers to submit their original contributions addressing particular challenging aspects of optimization algorithms from theoretical and applied viewpoints. The topics of this Special Issue include (but are not limited to) the following:

  • Optimization algorithms
  • Swarm intelligence
  • Meta-heuristics
  • Engineering applications
  • Engineering design problems
  • Feature selection
  • Image segmentation
  • Real-world applications
  • Constraint handling
  • Benchmarks
  • Novel Approaches
  • Complicated Optimization Problems
  • Industrial Problems

Prof. Dr. Heming Jia
Dr. Laith Abualigah
Guest Editors

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  • optimization algorithms
  • engineering application
  • metaheuristic algorithms

Published Papers (1 paper)

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Optimal Load Redistribution in Distribution Systems Using a Mixed-Integer Convex Model Based on Electrical Momentum
Information 2023, 14(4), 229; https://doi.org/10.3390/info14040229 - 07 Apr 2023
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This paper addresses the problem concerning the efficient minimization of power losses in asymmetric distribution grids from the perspective of convex optimization. This research’s main objective is to propose an approximation optimization model to reduce the total power losses in a three-phase network [...] Read more.
This paper addresses the problem concerning the efficient minimization of power losses in asymmetric distribution grids from the perspective of convex optimization. This research’s main objective is to propose an approximation optimization model to reduce the total power losses in a three-phase network using the concept of electrical momentum. To obtain a mixed-integer convex formulation, the voltage variables at each node are relaxed by assuming them to be equal to those at the substation bus. With this assumption, the power balance constraints are reduced to flow restrictions, allowing us to formulate a set of linear rules. The objective function is formulated as a strictly convex objective function by applying the concept of average electrical momentum, by representing the current flows in distribution lines as the active and reactive power variables. To solve the relaxed MIQC model, the GAMS software (Version 28.1.2) and its CPLEX, SBB, and XPRESS solvers are used. In order to validate the effectiveness of load redistribution in power loss minimization, the initial and final grid configurations are tested with the triangular-based power flow method for asymmetric distribution networks. Numerical results show that the proposed mixed-integer model allows for reductions of 24.34%, 18.64%, and 4.14% for the 8-, 15-, and 25-node test feeders, respectively, in comparison with the benchmark case. The sine–cosine algorithm and the black hole optimization method are also used for comparison, demonstrating the efficiency of the MIQC approach in minimizing the expected grid power losses for three-phase unbalanced networks. Full article
(This article belongs to the Special Issue Optimization Algorithms for Engineering Applications)
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