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Mathematics
Special Issues
Application of Mathematical Methods in Multi-Objective Optimization and Evolutionary Algorithms
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Application of Mathematical Methods in Multi-Objective Optimization and Evolutionary Algorithms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 1151

Special Issue Editor

Dr. Mariona Vila Bonilla

E-Mail Website
Guest Editor
Business Development and Entrepreneurship Research Department, EAE Business School, Barcelona, Spain
Interests: combinatorial optimization; optimization of manufacturing systems; supply chain design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are cordially invited to contribute to this Special Issue of Mathematics, focused on the application of mathematical methods in multi-objective optimization and evolutionary algorithms. In today’s complex landscape, decision making often requires a multi-dimensional approach that considers multiple criteria and objectives to achieve the best possible outcomes. Multi-objective optimization is, therefore, a highly relevant area of study, with applications in energy efficiency, supply chain design, data mining, telecommunications, and more.

Due to the complexity of multi-objective optimization problems, finding optimal solutions using exact methods can be computationally expensive and inefficient. As a result, heuristic and metaheuristic approaches have been developed, including evolutionary algorithms—techniques inspired by natural selection and competition for resources.

This Special Issue welcomes original research articles on both theoretical and applied aspects of multi-objective optimization and evolutionary algorithms across various sectors.

Dr. Mariona Vila Bonilla
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-objective optimization
  • evolutionary computation
  • multi-criteria analysis
  • metaheuristic algorithms
  • combinatorial optimization
  • multi-criteria decision making
  • genetic algorithms
  • particle swarm algorithms
  • artificial neural networks
  • renewable energy
  • green supply chain design
  • data mining

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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38 pages, 5909 KB  
Article
A Hybrid TLBO-Cheetah Algorithm for Multi-Objective Optimization of SOP-Integrated Distribution Networks
by Abdulaziz Alanazi, Mohana Alanazi and Mohammed Alruwaili
Mathematics 2025, 13(21), 3419; https://doi.org/10.3390/math13213419 - 27 Oct 2025
Viewed by 403
Abstract
The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer [...] Read more.
The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article
(This article belongs to the Special Issue Application of Mathematical Methods in Multi-Objective Optimization and Evolutionary Algorithms)
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30 pages, 488 KB  
Article
An Evolutionary Procedure for a Bi-Objective Assembly Line Balancing Problem
by Jordi Pereira and Mariona Vilà
Mathematics 2025, 13(20), 3336; https://doi.org/10.3390/math13203336 - 20 Oct 2025
Viewed by 500
Abstract
An assembly line is a manufacturing process commonly used in the production of commodity goods. The assembly process is divided into elementary tasks that are sequentially performed at serially arranged workstations. Among the various challenges that must be addressed during the design and [...] Read more.
An assembly line is a manufacturing process commonly used in the production of commodity goods. The assembly process is divided into elementary tasks that are sequentially performed at serially arranged workstations. Among the various challenges that must be addressed during the design and operation of an assembly line, the assembly line balancing problem involves the assignment of tasks to different workstations. In its simplest form, this problem aims to distribute assembly operations among the workstations efficiently. An efficient line is one that optimizes a specific objective function, usually associated with maximizing throughput or minimizing resource requirements. In this study, we adopt a bi-objective approach to find a Pareto set of efficient solutions balancing throughput and resource requirements. To address this problem, we propose a multi-objective evolutionary method, complemented by single- and multi-objective local search procedures that leverage a polynomially solvable case of the problem. We then compare the results of these methods, including their hybridizations, through a computational experiment demonstrating the ability to achieve high-quality solutions. Full article
(This article belongs to the Special Issue Application of Mathematical Methods in Multi-Objective Optimization and Evolutionary Algorithms)
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