Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
1.8
Journals
Algorithms
Special Issues
Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024
share announcement

Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 15434

Special Issue Editor

Prof. Dr. Massimiliano Caramia

E-Mail Website
Guest Editor
Department of Enterprise Engineering, University of Rome “Tor Vergata”, 00133 Roma, Italy
Interests: scheduling; graph theory; optimization; mathematical modeling; supply chain optimization; logistics; transportation; production systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The complexity of several real-life scenarios in which decision-making is involved often requires optimization methods and algorithms able to simultaneously cope with more than one objective. Generally, today, decisions at all levels (strategic, tactical, operative) require financial and economic goals to be correctly gathered with lean and green targets in order to find solutions able to produce profit and at the same time respect the environment and people’s safety. Multi-objective optimization is therefore sought to solve these kinds of applications, bearing in mind the hard computational effort they typically require. In accomplishing this task, on the one hand, it is important to continuously improve the implementation of existing approaches and, on the other hand, invest effort toward possible new approaches, also exploiting the rapid evolution of computer technologies.

The aim of this Special Issue is to collect original manuscripts dealing with multi-objective algorithms and optimization in the broad sense. We encourage the submission of original papers presenting innovative applications and/or contributing to the theory in this area. Some possible topics of interest are:

  • Evolutionary multi-objective optimization;
  • Game-theoretic approaches to multi-objective optimization;
  • Multi-level optimization;
  • Meta-heuristic methods for multi-objective optimization;
  • Hybrid approaches for multi-objective optimization;
  • Uncertainty handling in multi-objective optimization;
  • Parallelization techniques for multi-objective optimization.

We also encourage papers that investigate these topics in real-world applications, such as engineering design, production planning, healthcare, finance, and transportation, to name a few.

All submitted papers will go through a rigorous review process, and only manuscripts of the highest quality will be accepted for publication.

We look forward to receiving your contributions to this Special Issue.

Prof. Dr. Massimiliano Caramia
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Algorithms is an international peer-reviewed open access monthly 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 1600 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.

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

22 pages, 8170 KiB  
Article
Multi-Objective Resource-Constrained Scheduling in Large and Repetitive Construction Projects
by Vasiliki Lazari, Athanasios Chassiakos and Stylianos Karatzas
Algorithms 2024, 17(8), 351; https://doi.org/10.3390/a17080351 - 10 Aug 2024
Viewed by 1434
Abstract
Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still [...] Read more.
Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project’s size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

22 pages, 897 KiB  
Article
Pitfalls in Metaheuristics Solving Stoichiometric-Based Optimization Models for Metabolic Networks
by Mónica Fabiola Briones-Báez, Luciano Aguilera-Vázquez, Nelson Rangel-Valdez, Cristal Zuñiga, Ana Lidia Martínez-Salazar and Claudia Gomez-Santillan
Algorithms 2024, 17(8), 336; https://doi.org/10.3390/a17080336 - 1 Aug 2024
Viewed by 919
Abstract
Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the [...] Read more.
Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

20 pages, 424 KiB  
Article
Enhancing Program Synthesis with Large Language Models Using Many-Objective Grammar-Guided Genetic Programming
by Ning Tao, Anthony Ventresque, Vivek Nallur and Takfarinas Saber
Algorithms 2024, 17(7), 287; https://doi.org/10.3390/a17070287 - 1 Jul 2024
Viewed by 1153
Abstract
The ability to automatically generate code, i.e., program synthesis, is one of the most important applications of artificial intelligence (AI). Currently, two AI techniques are leading the way: large language models (LLMs) and genetic programming (GP) methods—each with its strengths and weaknesses. While [...] Read more.
The ability to automatically generate code, i.e., program synthesis, is one of the most important applications of artificial intelligence (AI). Currently, two AI techniques are leading the way: large language models (LLMs) and genetic programming (GP) methods—each with its strengths and weaknesses. While LLMs have shown success in program synthesis from a task description, they often struggle to generate the correct code due to ambiguity in task specifications, complex programming syntax, and lack of reliability in the generated code. Furthermore, their generative nature limits their ability to fix erroneous code with iterative LLM prompting. Grammar-guided genetic programming (G3P, i.e., one of the top GP methods) has been shown capable of evolving programs that fit a defined Backus–Naur-form (BNF) grammar based on a set of input/output tests that help guide the search process while ensuring that the generated code does not include calls to untrustworthy libraries or poorly structured snippets. However, G3P still faces issues generating code for complex tasks. A recent study attempting to combine both approaches (G3P and LLMs) by seeding an LLM-generated program into the initial population of the G3P has shown promising results. However, the approach rapidly loses the seeded information over the evolutionary process, which hinders its performance. In this work, we propose combining an LLM (specifically ChatGPT) with a many-objective G3P (MaOG3P) framework in two parts: (i) provide the LLM-generated code as a seed to the evolutionary process following a grammar-mapping phase that creates an avenue for program evolution and error correction; and (ii) leverage many-objective similarity measures towards the LLM-generated code to guide the search process throughout the evolution. The idea behind using the similarity measures is that the LLM-generated code is likely to be close to the correct fitting code. Our approach compels any generated program to adhere to the BNF grammar, ultimately mitigating security risks and improving code quality. Experiments on a well-known and widely used program synthesis dataset show that our approach successfully improves the synthesis of grammar-fitting code for several tasks. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

19 pages, 823 KiB  
Article
Elite Multi-Criteria Decision Making—Pareto Front Optimization in Multi-Objective Optimization
by Adarsh Kesireddy and F. Antonio Medrano
Algorithms 2024, 17(5), 206; https://doi.org/10.3390/a17050206 - 10 May 2024
Cited by 1 | Viewed by 1829
Abstract
Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic [...] Read more.
Optimization is a process of minimizing or maximizing a given objective function under specified constraints. In multi-objective optimization (MOO), multiple conflicting functions are optimized within defined criteria. Numerous MOO techniques have been developed utilizing various meta-heuristic methods such as Evolutionary Algorithms (EAs), Genetic Algorithms (GAs), and other biologically inspired processes. In a cooperative environment, a Pareto front is generated, and an MOO technique is applied to solve for the solution set. On other hand, Multi-Criteria Decision Making (MCDM) is often used to select a single best solution from a set of provided solution candidates. The Multi-Criteria Decision Making–Pareto Front (M-PF) optimizer combines both of these techniques to find a quality set of heuristic solutions. This paper provides an improved version of the M-PF optimizer, which is called the elite Multi-Criteria Decision Making–Pareto Front (eMPF) optimizer. The eMPF method uses an evolutionary algorithm for the meta-heuristic process and then generates a Pareto front and applies MCDM to the Pareto front to rank the solutions in the set. The main objective of the new optimizer is to exploit the Pareto front while also exploring the solution area. The performance of the developed method is tested against M-PF, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III). The test results demonstrate the performance of the new eMPF optimizer over M-PF, NSGA-II, and NSGA-III. eMPF was not only able to exploit the search domain but also was able to find better heuristic solutions for most of the test functions used. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

22 pages, 1016 KiB  
Article
Multi-Objective BiLevel Optimization by Bayesian Optimization
by Vedat Dogan and Steven Prestwich
Algorithms 2024, 17(4), 146; https://doi.org/10.3390/a17040146 - 30 Mar 2024
Viewed by 2205
Abstract
In a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower who reacts, each aiming [...] Read more.
In a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower who reacts, each aiming to optimize their own objective. Many real-world decision-making processes have various objectives to optimize at the same time while considering how the decision-makers affect each other. When both features are combined, we have a multi-objective bilevel optimization problem, which arises in manufacturing, logistics, environmental economics, defence applications and many other areas. Many exact and approximation-based techniques have been proposed, but because of the intrinsic nonconvexity and conflicting multiple objectives, their computational cost is high. We propose a hybrid algorithm based on batch Bayesian optimization to approximate the upper-level Pareto-optimal solution set. We also extend our approach to handle uncertainty in the leader’s objectives via a hypervolume improvement-based acquisition function. Experiments show that our algorithm is more efficient than other current methods while successfully approximating Pareto-fronts. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

19 pages, 5802 KiB  
Article
Test Center Location Problem: A Bi-Objective Model and Algorithms
by Mansoor Davoodi and Justin M. Calabrese
Algorithms 2024, 17(4), 135; https://doi.org/10.3390/a17040135 - 25 Mar 2024
Viewed by 1419
Abstract
The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, [...] Read more.
The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, has necessitated the development of customized solutions. This paper introduces a bi-objective integer linear programming model designed to achieve two key objectives: minimizing average travel time for individuals visiting testing centers and maximizing an equitable workload distribution among testing centers. This problem is NP-hard and we propose a customized local search algorithm based on the Voronoi diagram. Additionally, we employ an ϵ-constraint approach, which leverages the Gurobi solver. We rigorously examine the effectiveness of the model and the algorithms through numerical experiments and demonstrate their capability to identify Pareto-optimal solutions. We show that while the Gurobi performs efficiently in small-size instances, our proposed algorithm outperforms it in large-size instances of the problem. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

Review

Jump to: Research

46 pages, 1768 KiB  
Review
Multiobjective Path Problems and Algorithms in Telecommunication Network Design—Overview and Trends
by José Craveirinha, João Clímaco, Rita Girão-Silva and Marta Pascoal
Algorithms 2024, 17(6), 222; https://doi.org/10.3390/a17060222 - 22 May 2024
Viewed by 1207
Abstract
A major area of application of multiobjective path problems and resolution algorithms is telecommunication network routing design, taking into account the extremely rapid technological and service evolutions. The need for explicit consideration of heterogeneous Quality of Service metrics makes it advantageous for the [...] Read more.
A major area of application of multiobjective path problems and resolution algorithms is telecommunication network routing design, taking into account the extremely rapid technological and service evolutions. The need for explicit consideration of heterogeneous Quality of Service metrics makes it advantageous for the development of routing models where various technical–economic aspects, often conflicting, should be tackled. Our work is focused on multiobjective path problem formulations and resolution methods and their applications to routing methods. We review basic concepts and present main formulations of multiobjective path problems, considering different types of objective functions. We outline the different types of resolution methods for these problems, including a classification and overview of relevant algorithms concerning different types of problems. Afterwards, we outline background concepts on routing models and present an overview of selected papers considered as representative of different types of applications of multiobjective path problem formulations and algorithms. A broad characterization of major types of path problems relevant in this context is shown regarding the overview of contributions in different technological and architectural network environments. Finally, we outline research trends in this area, in relation to recent technological evolutions in communication networks. Full article
(This article belongs to the Special Issue Recent Advances in Multi-Objective Algorithms and Optimization 2023–2024)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop