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Computation
Special Issues
Applications of Evolutionary Computation: Past Success and Future Challenges
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Applications of Evolutionary Computation: Past Success and Future Challenges

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 8370

Special Issue Editor

Dr. Alexandros Tzanetos

E-Mail Website1 Website2
Guest Editor
Jönköping AI Lab (JAIL), Department of Computing, School of Engineering, Jönköping University, Gjuterigatan 5, 553 18 Jönköping, Sweden
Interests: evolutionary computation; nature-inspired algorithms; swarm intelligence; machine learning; operational research; optimization; combinatorial optimization; decision engineering; management engineering

Special Issue Information

Dear Colleagues,

Evolutionary computation (EC) counts nearly 50 years of research. During these decades, EC has been applied in many fields, such as engineering, operational research, finance, telecommunication, sports science, and, most recently, it is combined with machine learning.

This Special Issue aims to highlight the latest applications of EC. Therefore, I invite you to submit original studies applying EC in challenging real-world problems. Such studies may depict the ability of EC to provide near-optimal solutions in a reasonable amount of time when dealing with complex optimization tasks. Or they may introduce a hybrid scheme of EC methods to tackle problems more efficiently. Moreover, studies addressing the recent trend of evolutionary computation for machine learning are highly welcome.

Another aim of this Special Issue is to present some application areas in which EC has established its position as a powerful optimization tool. Thus, I also seek surveys that discuss the achievements of EC in various fields. Works providing a review of established EC algorithms fall within the scope of this Special Issue, too.

The Special Issue focuses on the applications of existing evolutionary/nature-inspired algorithms, not the introduction of new metaphor-based algorithms. Therefore, submissions proposing a new algorithm are discouraged.

Dr. Alexandros Tzanetos
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. Computation 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 1800 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 computing
  • memetic algorithms
  • nature-inspired algorithms
  • swarm intelligence
  • metaheuristics
  • optimization
  • machine learning

Published Papers (4 papers)

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Research

Jump to: Review

32 pages, 2003 KiB  
Article
Enhancing Algorithm Selection through Comprehensive Performance Evaluation: Statistical Analysis of Stochastic Algorithms
by Azad Arif Hama Amin, Aso M. Aladdin, Dler O. Hasan, Soran R. Mohammed-Taha and Tarik A. Rashid
Computation 2023, 11(11), 231; https://doi.org/10.3390/computation11110231 - 16 Nov 2023
Viewed by 1640
Abstract
Analyzing stochastic algorithms for comprehensive performance and comparison across diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a wide spectrum of test functions, including both classical benchmarks and CEC-C06 2019 conference functions, distinct patterns of performance emerge. In specific situations, [...] Read more.
Analyzing stochastic algorithms for comprehensive performance and comparison across diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a wide spectrum of test functions, including both classical benchmarks and CEC-C06 2019 conference functions, distinct patterns of performance emerge. In specific situations, underscoring the importance of choosing algorithms contextually. Additionally, researchers have encountered a critical issue by employing a statistical model randomly to determine significance values without conducting other studies to select a specific model for evaluating performance outcomes. To address this concern, this study employs rigorous statistical testing to underscore substantial performance variations between pairs of algorithms, thereby emphasizing the pivotal role of statistical significance in comparative analysis. It also yields valuable insights into the suitability of algorithms for various optimization challenges, providing professionals with information to make informed decisions. This is achieved by pinpointing algorithm pairs with favorable statistical distributions, facilitating practical algorithm selection. The study encompasses multiple nonparametric statistical hypothesis models, such as the Wilcoxon rank-sum test, single-factor analysis, and two-factor ANOVA tests. This thorough evaluation enhances our grasp of algorithm performance across various evaluation criteria. Notably, the research addresses discrepancies in previous statistical test findings in algorithm comparisons, enhancing result reliability in the later research. The results proved that there are differences in significance results, as seen in examples like Leo versus the FDO, the DA versus the WOA, and so on. It highlights the need to tailor test models to specific scenarios, as p-value outcomes differ among various tests within the same algorithm pair. Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation: Past Success and Future Challenges)
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26 pages, 1867 KiB  
Article
Improvement in Sizing Constrained Analog IC via Ts-CPD Algorithm
by Pedro Lagos-Eulogio, Pedro Miranda-Romagnoli, Juan Carlos Seck-Tuoh-Mora and Norberto Hernández-Romero
Computation 2023, 11(11), 230; https://doi.org/10.3390/computation11110230 - 16 Nov 2023
Viewed by 1352
Abstract
In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE) to include constrained optimization, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering [...] Read more.
In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE) to include constrained optimization, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a differential amplifier, a two-stage operational amplifier (op-amp), and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works. The Ts-CPD implementation was performed in Matlab using Ngspice and can be found on GitHub (see Data Availability Statement). Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation: Past Success and Future Challenges)
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39 pages, 6696 KiB  
Article
Marine Predators Algorithm for Sizing Optimization of Truss Structures with Continuous Variables
by Rafiq Bodalal and Farag Shuaeib
Computation 2023, 11(5), 91; https://doi.org/10.3390/computation11050091 - 30 Apr 2023
Cited by 4 | Viewed by 1806
Abstract
In this study, the newly developed Marine Predators Algorithm (MPA) is formulated to minimize the weight of truss structures. MPA is a swarm-based metaheuristic algorithm inspired by the efficient foraging strategies of marine predators in oceanic environments. In order to assess the robustness [...] Read more.
In this study, the newly developed Marine Predators Algorithm (MPA) is formulated to minimize the weight of truss structures. MPA is a swarm-based metaheuristic algorithm inspired by the efficient foraging strategies of marine predators in oceanic environments. In order to assess the robustness of the proposed method, three normal-sized structural benchmarks (10-bar, 60-bar, and 120-bar spatial dome) and three large-scale structures (272-bar, 942-bar, and 4666-bar truss tower) were selected from the literature. Results point to the inherent strength of MPA against all state-of-the-art metaheuristic optimizers implemented so far. Moreover, for the first time in the field, a quantitative evaluation and an answer to the age-old question of the proper convergence behavior (exploration vs. exploitation balance) in the context of structural optimization is conducted. Therefore, a novel dimension-wise diversity index is adopted as a methodology to investigate each of the two schemes. It was concluded that the balance that produced the best results was about 90% exploitation and 10% exploration (on average for the entire computational process). Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation: Past Success and Future Challenges)
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Review

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23 pages, 1829 KiB  
Review
Evolutionary Computation Techniques for Path Planning Problems in Industrial Robotics: A State-of-the-Art Review
by Martin Juříček, Roman Parák and Jakub Kůdela
Computation 2023, 11(12), 245; https://doi.org/10.3390/computation11120245 - 04 Dec 2023
Cited by 1 | Viewed by 1911
Abstract
The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice in industry. Evolutionary computation (EC) techniques are optimization methods [...] Read more.
The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice in industry. Evolutionary computation (EC) techniques are optimization methods that have found their use in diverse engineering fields. This state-of-the-art review focuses on recent developments and progress in their applications for industrial robotics, especially for path planning problems that need to satisfy various constraints that are implied by both the geometry of the robot and its surroundings. We discuss the most-used EC method and the modifications that suit this particular purpose, as well as the different simulation environments that are used for their development. Lastly, we outline the possible research gaps and the expected directions future research in this area will entail. Full article
(This article belongs to the Special Issue Applications of Evolutionary Computation: Past Success and Future Challenges)
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