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Advances in Evolutionary Computation and Applications
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Advances in Evolutionary Computation and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2919

Special Issue Editors

Dr. Dhananjay R. Thiruvady

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Guest Editor
School of Information Technology, Deakin University, Geelong, VIC 3125, Australia
Interests: optimisation; metaheuristics; swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Su Nguyen

E-Mail Website
Guest Editor
Centre for Data Analytics and Cognition, Bundoora, VIC 3086, Australia
Interests: optimisation; analytics; evolutionary computation
Dr. Yuan Sun

E-Mail Website
Guest Editor
Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia
Interests: operations research; metaheuristics; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

In recent years, evolutionary computation has been a proven approach for combinatorial optimisation. From the efficient modelling of several theoretical and applied problems to efficient incomplete search, evolutionary computation is at the forefront of AI-based optimisation. Moreover, the wide applicability of evolutionary computational methods, especially to real-world problems, demonstrates the need for the continued advancement of this field of research.

This Special Issue aims to bring together recent advances in evolutionary computation, with an emphasis on the application of evolutionary optimisation methods to real-world problems, the integration of evolutionary computation with methods from AI and operations research, and improving evolutionary computation via machine learning.

Areas of interest for this Special Issue include (but are not limited to):

  • Theoretical and applied studies in evolutionary computation;
  • Combining methods from operations research and evolutionary computation (metaheuristics);
  • Enhancing evolutionary computational methods via machine learning;
  • Applications to real-world and industrial problems;
  • Large-scale evolutionary computation.

Dr. Dhananjay R. Thiruvady
Dr. Su Nguyen
Dr. Yuan Sun
Guest Editors

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. 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

  • evolutionary algorithms
  • genetic algorithms
  • metaheuristics
  • multi-objective optimisation
  • scheduling
  • logistics
  • machine learning
  • large-scale optimisation

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Published Papers (2 papers)

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Research

26 pages, 6002 KiB  
Article
Multi-Objective Optimization in Support of Life-Cycle Cost-Performance-Based Design of Reinforced Concrete Structures
by Ali Sabbaghzade Feriz, Hesam Varaee and Mohammad Reza Ghasemi
Mathematics 2024, 12(13), 2008; https://doi.org/10.3390/math12132008 - 28 Jun 2024
Cited by 2 | Viewed by 994
Abstract
Surveys on the optimum seismic design of structures reveal that many investigations focus on minimizing initial costs while satisfying performance constraints. Although reducing initial costs while complying with earthquake design codes significantly ensures occupant safety, it may still cause considerable economic losses and [...] Read more.
Surveys on the optimum seismic design of structures reveal that many investigations focus on minimizing initial costs while satisfying performance constraints. Although reducing initial costs while complying with earthquake design codes significantly ensures occupant safety, it may still cause considerable economic losses and fatalities. Therefore, calculating potential earthquake damages over the structure’s lifetime is essential from an optimal Life-Cycle Cost (LCC) design perspective. LCC analysis evaluates economic feasibility, including construction, operation, occupancy, maintenance, and end-of-life costs. The population-based, meta-heuristic Ideal Gas Molecular Movement (IGMM) algorithm has proven effective in solving highly nonlinear mono- and multi-objective engineering problems. This paper investigates the LCC-based mono- and multi-objective optimum design of a 3D four-story concrete building structure using the Endurance Time (ET) method, which is employed for its efficiency in estimating structural responses under varying seismic hazard levels. The novelty of this work lies in integrating the ET method with the IGMM algorithm to comprehensively address both economic and performance criteria in seismic design. The results indicate that the proposed technique significantly reduces minor injury costs, rental costs, and income costs by 22%, 16%, and 16%, respectively, achieving a total reduction of 10% in all structural Life-Cycle Costs, which is considered significant. Full article
(This article belongs to the Special Issue Advances in Evolutionary Computation and Applications)
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17 pages, 1480 KiB  
Article
An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems
by Yifei Yang, Sichen Tao, Shibo Dong, Masahiro Nomura and Zheng Tang
Mathematics 2023, 11(17), 3733; https://doi.org/10.3390/math11173733 - 30 Aug 2023
Cited by 1 | Viewed by 1389
Abstract
The spherical evolution algorithm (SE) is a unique algorithm proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due to the challenges arising from the multiple choices [...] Read more.
The spherical evolution algorithm (SE) is a unique algorithm proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due to the challenges arising from the multiple choices of operators and the utilization of a spherical search method. In this paper, we introduce an enhancement method that incorporates weights in individuals’ dimensions that are affected by individual fitness during the iteration process, aiming to improve SE by adaptively balancing the tradeoff between exploitation and exploration during convergence. This is achieved by reducing the randomness of dimension selection and enhancing the retention of historical information in the iterative process of the algorithm. This new SE improvement algorithm is named DWSE. To evaluate the effectiveness of DWSE, in this study, we apply it to the CEC2017 standard test set, the CEC2013 large-scale global optimization test set, and 22 real-world problems from CEC2011. The experimental results substantiate the effectiveness of DWSE in achieving improvement. Full article
(This article belongs to the Special Issue Advances in Evolutionary Computation and Applications)
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