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Algorithms
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Algorithms in Multi-Objective Optimization
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Algorithms in Multi-Objective Optimization

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 2022) | Viewed by 16586

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,

Many real-world optimization problems typically involve multiple (conflicting) objectives. In such problems, the aim is to find the set of non-dominated (Pareto-optimal) solutions, producing difference image vectors which are indifferent to each other when no other selection criterion is available. Determining the whole set of Pareto-optimal solutions, as well as its image, i.e., the Pareto-front, is a difficult problem even though objectives and constraints are linear. Some algorithms (e.g., Weighting method and -Constraints method), in the attempt to (fully or partially) accomplish this task, rely on iteratively solving proper single-objective mathematical formulations, derived from the original problem, each one returning a non-dominated solution, possibly requiring a large computing time when such a single-objective problem is NP-hard. Other solution approaches, like Evolutionary Algorithms, due to their inherent parallelism, have the potential of finding multiple Pareto-optimal solutions in a single run. Other classes of algorithms (e.g., Utility Function method, Lexicographic method, Goal Programming), even more differently, try to overcome the burden hidden behind finding the Pareto-front by reducing the multi-objective problem into a unique single-objective problem, assuming the knowledge of additional information, e.g., an utility value for each solution or a ranking among the objectives.

The aim of this Special Issue is to collect original manuscripts dealing with algorithms in multi-objective optimization; in particular, two types of original manuscripts are welcome, i.e., Innovative Applications Papers, describing novel ways to solve real world multi-objective optimization problems, and Theory and Methodology Papers, presenting original research results contributing to the methodology of solving multi-objective optimization and to its theoretical foundations.

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.

Keywords

  • Non-dominated Solutions
  • Pareto Front
  • Optimization
  • Mathematical Programming
  • Heuristics
  • Metaheuristics
  • Matheuristics
  • Exact Algorithms

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Published Papers (5 papers)

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Editorial

Jump to: Research

3 pages, 169 KiB  
Editorial
“Algorithms in Multi-Objective Optimization”: Foreword by the Guest Editor
by Massimiliano Caramia
Algorithms 2022, 15(12), 476; https://doi.org/10.3390/a15120476 - 15 Dec 2022
Viewed by 1106
Abstract
Many real-world optimization problems typically involve multiple (conflicting) objectives [...] Full article
(This article belongs to the Special Issue Algorithms in Multi-Objective Optimization)

Research

Jump to: Editorial

24 pages, 3763 KiB  
Article
A Multi-Objective Model and Algorithms of Aggregate Production Planning of Multi-Product with Early and Late Delivery
by Lanfen Liu and Xinfeng Yang
Algorithms 2022, 15(6), 182; https://doi.org/10.3390/a15060182 - 25 May 2022
Cited by 4 | Viewed by 2232
Abstract
Due to the influence of insufficient production capacity or shortage of production materials, production enterprises may produce products in advance or be backordered. In order to improve the adaptability of enterprises and reduce production costs, the impacts of early delivery and delayed delivery [...] Read more.
Due to the influence of insufficient production capacity or shortage of production materials, production enterprises may produce products in advance or be backordered. In order to improve the adaptability of enterprises and reduce production costs, the impacts of early delivery and delayed delivery are analyzed, and the method to determine the loss threshold is put forward. Moreover, the maximum allowable shortage of customers with different tardiness is calculated, and the cost of delayed delivery and loss of sales is determined. Considering the production cost, raw material cost, inventory cost, staff cost, stockout, and lost sales cost, an early/delay multi-objective optimization model is developed for an aggregate production planning (APP) problem to minimize total production costs and instability in the workforce. Three algorithms and three different hybrid strategies are designed to solve the model. Finally, some test experiments are employed in order to validate the performance of the proposed evaluation of the three algorithms. The results show that: The method of determining the loss threshold can effectively reflect the double influence of customer satisfaction with waiting time and shortage quantity. The definition of unit tardiness cost reflects the law that it increases gradually with waiting time. The determination of the feasible range of product output and the number of workers in the workforce can reduce the search scope of the algorithm and improve the efficiency of the algorithm. Full article
(This article belongs to the Special Issue Algorithms in Multi-Objective Optimization)
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19 pages, 1638 KiB  
Article
Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization
by Dhanalakshmi Bettahalli Kengegowda, Srikantaiah Kamidoddi Chowdaiah, Gururaj Harinahalli Lokesh and Francesco Flammini
Algorithms 2022, 15(2), 70; https://doi.org/10.3390/a15020070 - 21 Feb 2022
Cited by 3 | Viewed by 2555
Abstract
Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective [...] Read more.
Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resource utilization and cost optimization in the cloud. CMRBC has two benefits. Firstly, this is a cost-effective solution to service providers and customers. Secondly, for every job, virtual machine (VM) creations are avoided to reduce brokerage. The allocation, creation or selection of resources of VM is carried out by broker. The main objective is to maximize the resource utilization and minimize brokerage in cloud computing by using Multi-Objective Optimization (MOO). It considered a multi-attribute approach as it has more than two objectives. Likewise, CMRBC implements efficient resource allocation to reduce the usage cost of resources. The outcome of the experiment shows that CMRBC outperforms 60 percent of reduction in brokerage and 10 percent in response time. Full article
(This article belongs to the Special Issue Algorithms in Multi-Objective Optimization)
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14 pages, 2953 KiB  
Article
Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
by Naomi Simumba, Suguru Okami, Akira Kodaka and Naohiko Kohtake
Algorithms 2021, 14(9), 260; https://doi.org/10.3390/a14090260 - 30 Aug 2021
Cited by 4 | Viewed by 2458
Abstract
Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, [...] Read more.
Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios. Full article
(This article belongs to the Special Issue Algorithms in Multi-Objective Optimization)
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14 pages, 439 KiB  
Article
A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients
by Amr Mohamed AbdelAziz, Louai Alarabi, Saleh Basalamah and Abdeltawab Hendawi
Algorithms 2021, 14(2), 38; https://doi.org/10.3390/a14020038 - 27 Jan 2021
Cited by 15 | Viewed by 6080
Abstract
The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get [...] Read more.
The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems. Full article
(This article belongs to the Special Issue Algorithms in Multi-Objective Optimization)
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