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Swarm and Evolutionary Computation—Bridging Theory and Practice

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

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 23619

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Special Issue Editors

Prof. Dr. Yong-Hyuk Kim

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Guest Editor

School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
Interests: operations research; combinatorial optimization; swarm and evolutionary computation; heuristic optimization algorithm

Dr. Fabio Caraffini

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Guest Editor

Department of Computer Science, Computational Foundry, Swansea University, Bay Campus, Fabian Way, Skewen SA1 8EN, UK
Interests: evolutionary computation; swarm intelligence; computational intelligence; differential evolution; memetic computing
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Special Issue Information

Dear Colleagues,

Swarm and evolutionary computation (SEC) is a broad and growing area of modern Computer Sciences, dealing with nature-inspired systems that are capable of displaying intelligent behaviour, thus optimising a vast range of challenging real-world scenarios that cannot be addressed via the direct application of purely theoretical exact approaches.

For decades, the swarm intelligence and the evolutionary computation communities worked independently and, despite having common goals, progressed as two separate fields. Currently, advances in these research topics have generated highly hybrid, interconnected and self-adaptive frameworks displaying and employing ideas from both fields. This calls for more collaborative and joint efforts to be made by SEC researchers and practitioners from relevant fields, e.g., engineering, robotics and TLC.

Indeed, research SEC is highly applicable to several real-world domains, from engineering to finance, and other scenarios in which optimisation is needed to either make an intelligent decision or minimise/maximise costs/profits.

Not to be underestimated, SEC systems currently play a key role in related Computer Science areas, such as Machine Learning (ML) and Deep Learning (DL), where hybrid methods can either make use of SEC algorithms to optimise, train or design ML and DL systems or, vice-versa, make use of ML to increase the efficiency of non-conforming SEC and help them overcome undesired algorithm behaviour, e.g., premature convergence, lack of selection pressure, or difficulties in preserving an adequate level of population diversity.

In this light, regardless the nature of the problem at hand, e.g., single-objective rather than multi-objective, dynamic rather than static, or continuous rather than discrete, the aim of this Special Issue is to gather a collection of articles reflecting the latest developments within the SEC community both in terms of successful real-world applications and in terms of state-of-the-art algorithmic design. We also encourage submissions of studies investigating the algorithmic behaviour and dynamics of SEC methods.

Topics of interest

We welcome articles where theoretical or AI methods are used in conjunction with SEC methods to address the relevant topic of interests, as suggested below:

Theoretical or geometric aspects of search space and fitness landscape;
Computational intelligence and SEC -based optimisation;
Evolutionary machine learning;
Neuroevolution;
Surrogate modelling;
Operations research and SEC systems;
Forecasting and data mining with SEC algorithms;
Real-world applications (including scheduling problems, financial engineering, etc.).

Prof. Dr. Yong-Hyuk Kim
Dr. Fabio Caraffini
Guest Editors

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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.

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Keywords

Evolutionary algorithms
Genetic programming
Swarm intelligence
Meta-heuristics
Memetic algorithms
Genetic algorithms
Differential evolution
Ant colony optimisation
Large-scale optimisation

Published Papers (12 papers)

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Editorial

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3 pages, 180 KiB

Open AccessEditorial

Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”

by Yong-Hyuk Kim and Fabio Caraffini

Mathematics 2023, 11(5), 1209; https://doi.org/10.3390/math11051209 - 01 Mar 2023

Viewed by 998

Abstract

Swarm and evolutionary computation (SEC) [...] Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

Research

Jump to: Editorial

16 pages, 2493 KiB

Open AccessArticle

Optimal Agent Search Using Surrogate-Assisted Genetic Algorithms

by Seung-Soo Shin and Yong-Hyuk Kim

Mathematics 2023, 11(1), 230; https://doi.org/10.3390/math11010230 - 02 Jan 2023

Cited by 1 | Viewed by 1506

Abstract

An intelligent agent is a program that can make decisions or perform a service based on its environment, user input, and experiences. Due to the complexity of its state and action spaces, agents are approximated by deep neural networks (DNNs), and it can be optimized using methods such as deep reinforcement learning and evolution strategies. However, these methods include simulation-based evaluations in the optimization process, and they are inefficient if the simulation cost is high. In this study, we propose surrogate-assisted genetic algorithms (SGAs), whose surrogate models are used in the fitness evaluation of genetic algorithms, and the surrogates also predict cumulative rewards for an agent’s DNN parameters. To improve the SGAs, we applied stepwise improvements that included multiple surrogates, data standardization, and sampling with dimensional reduction. We conducted experiments using the proposed SGAs in benchmark environments such as cart-pole balancing and lunar lander, and successfully found optimal solutions and significantly reduced computing time. The computing time was reduced by 38% and 95%, in the cart-pole balancing and lunar lander problems, respectively. For the lunar lander problem, an agent with approximately 4% better quality than that found by a gradient-based method was even found. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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16 pages, 2674 KiB

Open AccessArticle

Industrial Demand-Side Management by Means of Differential Evolution Considering Energy Price and Labour Cost

by Alessandro Niccolai, Gaia Gianna Taje, Davide Mosca, Fabrizio Trombello and Emanuele Ogliari

Mathematics 2022, 10(19), 3605; https://doi.org/10.3390/math10193605 - 02 Oct 2022

Cited by 2 | Viewed by 1132

Abstract

In the context of the high dependency on fossil fuels, the strong efforts aiming to shift towards a more sustainable world are having significant economic and political impacts. The electricity market is now encouraging prosumers to consume their own production, and thus reduce grid exchanges. Self-consumption can be increased using storage systems or rescheduling the loads. This effort involves not only residential prosumers but also industrial ones. The rescheduling process is an optimisation problem that can be effectively solved with evolutionary algorithms (EAs). In this paper, a specific procedure for bridging demand-side management from the theoretical application to a practical industrial scenario was introduced. In particular, the toroidal correction was used in the differential evolution with the aim of preventing the local minima worsening the effectiveness of the proposed method. Moreover, to achieve reasonable solutions, two different cost contributions have been considered: the energy cost and the labour cost. The method was tested on real data from a historical textile factory, Ratti S.p.A. Due to the nature of the loads, the design variables were the starting time of the 30 shiftable loads. The application of this procedure achieves a reduction in the total cost of approximately 99,500 EUR/year. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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30 pages, 3687 KiB

Open AccessArticle

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

by Dijana Jovanovic, Milos Antonijevic, Milos Stankovic, Miodrag Zivkovic, Marko Tanaskovic and Nebojsa Bacanin

Mathematics 2022, 10(13), 2272; https://doi.org/10.3390/math10132272 - 29 Jun 2022

Cited by 56 | Viewed by 4026

Abstract

Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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18 pages, 4450 KiB

Open AccessArticle

Evolutionary Exploration of Mechanical Assemblies in VR

by Won Gyu Kim and Kang Hoon Lee

Mathematics 2022, 10(8), 1232; https://doi.org/10.3390/math10081232 - 08 Apr 2022

Cited by 1 | Viewed by 1474

Abstract

Due to the maker movement and 3D printers, people nowadays can directly fabricate mechanical devices that meet their own objectives. However, it is not intuitive to identify the relationship between specific mechanical movements and mechanical structures that facilitate such movements. This paper presents an interactive system that can enable users to easily create and experiment with desired mechanical assemblies via direct manipulation interfaces in virtual reality, as well as to intuitively explore design space through repeated application of the crossover operation, which is used at the core of the genetic algorithm. Specifically, a mechanical assembly in our system is genetically encoded as a undirected graph structure in which each node corresponds to a mechanical part and each edge represents the connection between parts. As the user selects two different mechanical assemblies and commands the crossover operation, each of their corresponding graphs is split into two subgraphs and those subgraphs are recombined to generate the next-generation mechanical assemblies. The user can visually examine new mechanical assemblies, save assemblies that are closer to objectives, and remove the others. Based on our experiments, in which non-expert participants were asked to achieve a challenging design objective, it was verified that the proposed interface exhibited significantly effective performance. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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20 pages, 540 KiB

Open AccessArticle

Genetic Mean Reversion Strategy for Online Portfolio Selection with Transaction Costs

by Seung-Hyun Moon and Yourim Yoon

Mathematics 2022, 10(7), 1073; https://doi.org/10.3390/math10071073 - 26 Mar 2022

Cited by 4 | Viewed by 2221

Abstract

Online portfolio selection (OLPS) is a procedure for allocating portfolio assets using only past information to maximize an expected return. There have been successful mean reversion strategies that have achieved large excess returns on the traditional OLPS benchmark datasets. We propose a genetic mean reversion strategy that evolves a population of portfolio vectors using a hybrid genetic algorithm. Each vector represents the proportion of the portfolio assets, and our strategy chooses the best vector in terms of the expected returns on every trading day. To test our strategy, we used the price information of the S&P 500 constituents from 2000 to 2017 and compared various strategies for online portfolio selection. Our hybrid genetic framework successfully evolved the portfolio vectors; therefore, our strategy outperformed the other strategies when explicit or implicit transaction costs were incurred. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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15 pages, 285 KiB

Open AccessArticle

A Memetic Algorithm with a Novel Repair Heuristic for the Multiple-Choice Multidimensional Knapsack Problem

by Jaeyoung Yang, Yong-Hyuk Kim and Yourim Yoon

Mathematics 2022, 10(4), 602; https://doi.org/10.3390/math10040602 - 16 Feb 2022

Cited by 8 | Viewed by 2051

Abstract

We propose a memetic algorithm for the multiple-choice multidimensional knapsack problem (MMKP). In this study, we focus on finding good solutions for the MMKP instances, for which feasible solutions rarely exist. To find good feasible solutions, we introduce a novel repair heuristic based on the tendency function and a genetic search for the function approximation. Even when the density of feasible solutions over the entire solution space is very low, the proposed repair heuristic could successfully change infeasible solutions into feasible ones. Based on the proposed repair heuristic and effective local search, we designed a memetic algorithm that performs well on problem instances with a low density of feasible solutions. By performing experiments, we could show the superiority of our method compared with previous genetic algorithms. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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17 pages, 508 KiB

Open AccessArticle

Search Graph Magnification in Rapid Mixing of Markov Chains Associated with the Local Search-Based Metaheuristics

by Ajitha K. B. Shenoy and Smitha N. Pai

Mathematics 2022, 10(1), 47; https://doi.org/10.3390/math10010047 - 24 Dec 2021

Cited by 2 | Viewed by 2163

Abstract

The structural property of the search graph plays an important role in the success of local search-based metaheuristic algorithms. Magnification is one of the structural properties of the search graph. This study builds the relationship between the magnification of a search graph and the mixing time of Markov Chain (MC) induced by the local search-based metaheuristics on that search space. The result shows that the ergodic reversible Markov chain induced by the local search-based metaheuristics is inversely proportional to magnification. This result indicates that it is desirable to use a search space with large magnification for the optimization problem in hand rather than using any search spaces. The performance of local search-based metaheuristics may be good on such search spaces since the mixing time of the underlying Markov chain is inversely proportional to the magnification of search space. Using these relations, this work shows that MC induced by the Metropolis Algorithm (MA) mixes rapidly if the search graph has a large magnification. This indicates that for any combinatorial optimization problem, the Markov chains associated with the MA mix rapidly i.e., in polynomial time if the underlying search graph has large magnification. The usefulness of the obtained results is illustrated using the

0 / 1

-Knapsack Problem, which is a well-studied combinatorial optimization problem in the literature and is NP-Complete. Using the theoretical results obtained, this work shows that Markov Chains (MCs) associated with the local search-based metaheuristics like random walk and MA for

0 / 1

-Knapsack Problem mixes rapidly. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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20 pages, 546 KiB

Open AccessArticle

Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm

by Jaehyeong Lee, Hyuk Jang, Sungmin Ha and Yourim Yoon

Mathematics 2021, 9(21), 2813; https://doi.org/10.3390/math9212813 - 05 Nov 2021

Cited by 24 | Viewed by 6257

Abstract

Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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19 pages, 1546 KiB

Open AccessArticle

Genetic Feature Selection Applied to KOSPI and Cryptocurrency Price Prediction

by Dong-Hee Cho, Seung-Hyun Moon and Yong-Hyuk Kim

Mathematics 2021, 9(20), 2574; https://doi.org/10.3390/math9202574 - 14 Oct 2021

Cited by 11 | Viewed by 2416

Abstract

Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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18 pages, 2822 KiB

Open AccessArticle

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

by Mohammad Khishe, Fabio Caraffini and Stefan Kuhn

Mathematics 2021, 9(9), 1002; https://doi.org/10.3390/math9091002 - 28 Apr 2021

Cited by 39 | Viewed by 2782

Abstract

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to

99.11 %

, thus being particularly suitable for the early detection of COVID-19. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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24 pages, 2084 KiB

Open AccessArticle

A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

by Yenny Villuendas-Rey, Eley Barroso-Cubas, Oscar Camacho-Nieto and Cornelio Yáñez-Márquez

Mathematics 2021, 9(7), 786; https://doi.org/10.3390/math9070786 - 06 Apr 2021

Cited by 2 | Viewed by 1737

Abstract

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data. Full article

(This article belongs to the Special Issue Swarm and Evolutionary Computation—Bridging Theory and Practice)

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