Submit to Special Issue Submit Abstract to Special Issue Review for Mathematics Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Print Special Issue Flyer
Editor
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2027 | Viewed by 3731

Share This 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-anonymized 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

56 pages, 87040 KB

Open AccessArticle

Logistics-Supply-Chain-Enhanced Human Urbanization Algorithm for Global Optimization and Engineering Applications

by Zheming Zhang and Fan Liu

Mathematics 2026, 14(17), 3053; https://doi.org/10.3390/math14173053 - 25 Aug 2026

Viewed by 314

Abstract

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

(This article belongs to the Special Issue Application of Mathematical Methods in Multi-Objective Optimization and Evolutionary Algorithms)

► Show Figures

Figure 1

54 pages, 9223 KB

Open AccessArticle

An Improved Coati Optimization Algorithm with Urban-Traffic-Inspired Strategies for Global Optimization and Low-Carbon Microgrid Scheduling

by Wenjie Zhao and Chengpeng Li

Mathematics 2026, 14(16), 2926; https://doi.org/10.3390/math14162926 - 13 Aug 2026

Viewed by 190

Abstract

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems. Full article

(This article belongs to the Special Issue Application of Mathematical Methods in Multi-Objective Optimization and Evolutionary Algorithms)

► Show Figures

Figure 1

38 pages, 5909 KB

Open AccessArticle

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 1052

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 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)

► Show Figures

Figure 1

30 pages, 488 KB

Open AccessFeature PaperArticle

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 1455

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 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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI