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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (10 July 2022) | Viewed by 33912

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Prof. Dr. Lorenzo Salas-Morera

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Guest Editor

Area of Project Engineering, University of Cordoba, 14071 Córdoba, Spain
Interests: UA-FLP; plant layout design; evolutionary algorithms; interactive algorithms; project management
Special Issues, Collections and Topics in MDPI journals

Dr. Laura Garcia-Hernandez

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Guest Editor

Department of Rural Engineering, University of Córdoba, Av. de Medina Azahara, 5, 14071 Córdoba, Spain
Interests: UA-FLP; evolutionary algorithms; engineering education; project management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Metaheuristic algorithms have been extensively used for solving any kind of problems in a wide range of fields such as industrial applications, communications, economics, chemistry, medicine, etc., and they are specifically indicated in complex problems with high computational cost. Furthermore, there are a great variety of different strategies and applications and every day appear new improvements and ideas that contribute to enrich their exploitation possibilities in real problems.

We invite you to submit high-quality papers to this Special Issue on “Metaheuristic Algorithms and Applications”, with subjects covering the whole range from theory to applications. The following is a (non-exhaustive) list of topics of interests:

Swarm intelligence such as Artificial Bee Colony, Ant Colony Optimization, Particle Swarm Optimization and etc.
Nature-inspired metaheuristic algorithms such as Evolutionary Algorithm, Genetic Algorithm, etc
Neighborhood search algorithms such as Iterated Local Search, Simulated Annealing, Tabu Search, Variable Neighborhood Search, etc.
New metaheuristic frameworks/approaches/operators
Hybrid evolutionary algorithms
Agent-based evolutionary approaches
Novel bio-inspired algorithms
Empirical and theoretical research of metaheuristics
Automatic configuration of metaheuristics

Prof. Dr. Lorenzo Salas-Morera
Prof. Dr. Laura Garcia-Hernandez
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. 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

Evolutionary algorithms
Facility Layout Planning
Scheduling Problems
Meta-heuristics
Interactive Algorithms
Bio-inspired Algorithms
Swarm Intelligence

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Published Papers (12 papers)

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Research

21 pages, 1125 KiB

Open AccessArticle

A Neuroevolutionary Model to Estimate the Tensile Strength of Manufactured Parts Made by 3D Printing

by Matheus Alencar da Silva, Bonfim Amaro Junior, Ramon Rudá Brito Medeiros and Plácido Rogério Pinheiro

Algorithms 2022, 15(8), 263; https://doi.org/10.3390/a15080263 - 28 Jul 2022

Cited by 1 | Viewed by 1899

Abstract

Three-dimensional printing has advantages, such as an excellent flexibility in producing parts from the digital model, enabling the fabrication of different geometries that are both simple or complex, using low-cost materials and generating little residue. Many technologies have gained space, highlighting the artificial intelligence (AI), which has several applications in different areas of knowledge and can be defined as any technology that allows a system to demonstrate human intelligence. In this context, machine learning uses artificial intelligence to develop computational techniques, aiming to build knowledge automatically. This system is responsible for making decisions based on experiences accumulated through successful solutions. Thus, this work aims to develop a neuroevolutionary model using artificial intelligence techniques, specifically neural networks and genetic algorithms, to predict the tensile strength in materials manufactured by fused filament fabrication (FFF)-type 3D printing. We consider the collection and construction of a database on three-dimensional instances to reach our objective. To train our model, we adopted some parameters. The model algorithm was developed in the Python programming language. After analyzing the data and graphics generated by the execution of the tests, we present that the model outperformed, with a determination coefficient superior to 90%, resulting in a high rate of assertiveness. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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13 pages, 1958 KiB

Open AccessArticle

Pendulum Search Algorithm: An Optimization Algorithm Based on Simple Harmonic Motion and Its Application for a Vaccine Distribution Problem

by Nor Azlina Ab. Aziz and Kamarulzaman Ab. Aziz

Algorithms 2022, 15(6), 214; https://doi.org/10.3390/a15060214 - 17 Jun 2022

Cited by 6 | Viewed by 2847

Abstract

The harmonic motion of pendulum swinging centered at a pivot point is mimicked in this work. The harmonic motion’s amplitude at both side of the pivot are equal, damped, and decreased with time. This behavior is mimicked by the agents of the pendulum search algorithm (PSA) to move and look for an optimization solution within a search area. The high amplitude at the beginning encourages exploration and expands the search area while the small amplitude towards the end encourages fine-tuning and exploitation. PSA is applied for a vaccine distribution problem. The extended SEIR model of Hong Kong’s 2009 H1N1 influenza epidemic is adopted here. The results show that PSA is able to generate a good solution that is able to minimize the total infection better than several other methods. PSA is also tested using 13 multimodal functions from the CEC2014 benchmark function. To optimize multimodal functions, an algorithm must be able to avoid premature convergence and escape from local optima traps. Hence, the functions are chosen to validate the algorithm as a robust metaheuristic optimizer. PSA is found to be able to provide low error values. PSA is then benchmarked with the state-of-the-art particle swarm optimization (PSO) and sine cosine algorithm (SCA). PSA is better than PSO and SCA in a greater number of test functions; these positive results show the potential of PSA. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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15 pages, 4350 KiB

Open AccessArticle

Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition

by Jackson Horlick Teng, Thian Song Ong, Tee Connie, Kalaiarasi Sonai Muthu Anbananthen and Pa Pa Min

Algorithms 2022, 15(5), 161; https://doi.org/10.3390/a15050161 - 11 May 2022

Cited by 2 | Viewed by 2730

Abstract

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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16 pages, 4565 KiB

Open AccessArticle

Optimal Open-Loop Control of Discrete Deterministic Systems by Application of the Perch School Metaheuristic Optimization Algorithm

by Andrei V. Panteleev and Anna A. Kolessa

Algorithms 2022, 15(5), 157; https://doi.org/10.3390/a15050157 - 7 May 2022

Cited by 5 | Viewed by 2077

Abstract

A new hybrid metaheuristic method for optimizing the objective function on a parallelepiped set of admissible solutions is proposed. It mimics the behavior of a school of river perch when looking for food. The algorithm uses the ideas of several methods: a frog-leaping method, migration algorithms, a cuckoo algorithm and a path-relinking procedure. As an application, a wide class of problems of finding the optimal control of deterministic discrete dynamical systems with a nonseparable performance criterion is chosen. For this class of optimization problems, it is difficult to apply the discrete maximum principle and its generalizations as a necessary optimality condition and the Bellman equation as a sufficient optimality condition. The desire to extend the class of problems to be solved to control problems of trajectory bundles and stochastic problems leads to the need to use not only classical adaptive random search procedures, but also new approaches combining the ideas of migration algorithms and swarm intelligence methods. The efficiency of this method is demonstrated and an analysis is performed by solving several optimal deterministic discrete control problems: two nonseparable problems (Luus–Tassone and LiHaimes) and five classic linear systems control problems with known exact solutions. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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21 pages, 579 KiB

Open AccessArticle

Neuroevolution for Parameter Adaptation in Differential Evolution

by Vladimir Stanovov, Shakhnaz Akhmedova and Eugene Semenkin

Algorithms 2022, 15(4), 122; https://doi.org/10.3390/a15040122 - 7 Apr 2022

Cited by 7 | Viewed by 2522

Abstract

Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is considered. The artificial neural networks in this study are used for setting the scaling factor and crossover rate values based on the available information about the algorithm performance and previous successful values. The training is performed on a set of benchmark problems, and the testing and comparison is performed on several different benchmarks to evaluate the generalizing ability of the approach. The neuroevolution is enhanced with lexicase selection to handle the noisy fitness landscape of the benchmarking results. The experimental results show that it is possible to design efficient parameter adaptation techniques comparable to state-of-the-art methods, although such an automatic search for heuristics requires significant computational effort. The automatically designed solutions can be further analyzed to extract valuable knowledge about parameter adaptation. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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12 pages, 1291 KiB

Open AccessArticle

Dynamic Layout Design Optimization to Improve Patient Flow in Outpatient Clinics Using Genetic Algorithms

by Jyoti R. Munavalli, Shyam Vasudeva Rao, Aravind Srinivasan and Frits Van Merode

Algorithms 2022, 15(3), 85; https://doi.org/10.3390/a15030085 - 6 Mar 2022

Cited by 5 | Viewed by 3885

Abstract

Evolutionary algorithms, such as genetic algorithms have been used in various optimization problems. In this paper, we propose to apply this algorithm to obtain the layout design/redesign in order to improve the patient flow in an outpatient clinic. Layout designs are planned considering long-term requirements whereas the layout keeps modifying as per short-term demands. Over a period of time, the layout often does not remain efficient. Therefore, there is a need for such a model that helps in decision making on layout redesigns, and it must also optimize workflow by incorporating the flow constraints. In this study, we propose to minimize the waiting times by obtaining optimal and sub-optimal layout designs. A genetic algorithm is implemented to redesign the layouts based on the changing dynamics of patient demand, clinical pathways and services offered. The workflow is simulated with current layout and optimized layouts, and the results in terms of waiting time and cycle time are compared. The study shows that when layout design or redesign incorporate the workflow and pathways along with associated constraints, improves waiting time and cycle time of patients in the outpatient clinic. The distance between the departments/locations is translated to travelling time and overall travel distance/time is minimized by rearranging the allocations of departments to the location through genetic algorithms. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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13 pages, 3573 KiB

Open AccessArticle

Application of Mini-Batch Metaheuristic Algorithms in Problems of Optimization of Deterministic Systems with Incomplete Information about the State Vector

by Andrei V. Panteleev and Aleksandr V. Lobanov

Algorithms 2021, 14(11), 332; https://doi.org/10.3390/a14110332 - 14 Nov 2021

Cited by 6 | Viewed by 1848

Abstract

In this paper, we consider the application of the zero-order mini-batch optimization method in the problem of finding optimal control of a pencil of trajectories of nonlinear deterministic systems in the case of incomplete information about the state vector. The pencil of trajectories originates from a given set of initial states. To solve the problem, the structure of a feedback system is proposed, which contains models of the plant, measuring system, nonlinear state observer and control law of the fixed structure with unknown coefficients. The objective function proposed considers the quality of pencil of trajectories control, which is estimated by the average value of the Bolz functional over the given set of initial states. Unknown control laws of a plant and an observer are found in the form of expansions in terms of orthonormal systems of basis functions, which are specified on the set of possible states of a dynamical system. The original pencil of trajectories control problem is reduced to a global optimization problem, which is solved using the well-proven zero-order method, which uses a modified mini-batch approach in a random search procedure with adaptation. An algorithm for solving the problem is proposed. The satellite stabilization problem with incomplete information is solved. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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14 pages, 659 KiB

Open AccessArticle

An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities

by Alan McKendall and Artak Hakobyan

Algorithms 2021, 14(11), 306; https://doi.org/10.3390/a14110306 - 23 Oct 2021

Cited by 1 | Viewed by 2447

Abstract

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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22 pages, 4366 KiB

Open AccessArticle

Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

by Juan Chen, Qinxuan Feng and Qi Guo

Algorithms 2021, 14(9), 266; https://doi.org/10.3390/a14090266 - 13 Sep 2021

Cited by 4 | Viewed by 1961

Abstract

In order to solve the problem of traffic congestion and emission optimization of urban multi-class expressways, a robust dynamic nondominated sorting multi-objective genetic algorithm DFCM-RDNSGA-III based on density fuzzy c-means clustering method is proposed in this paper. Considering the three performance indicators of travel time, ramp queue and traffic emissions, the ramp metering and variable speed limit control schemes of an expressway are optimized to improve the main road and ramp traffic congestion, therefore achieving energy conservation and emission reduction. In the VISSIM simulation environment, a multi-on-ramp and multi-off-ramp road network is built to verify the performance of the algorithm. The results show that, compared with the existing algorithm NSGA-III, the DFCM-RDNSGA-III algorithm proposed in this paper can provide better ramp metering and variable speed limit control schemes in the process of road network peak formation and dissipation. In addition, the traffic congestion of expressways can be improved and energy conservation as well as emission reduction can also be realized. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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14 pages, 3613 KiB

Open AccessArticle

Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

by Zhenyu Song, Xuemei Yan, Lvxing Zhao, Luyi Fan, Cheng Tang and Junkai Ji

Algorithms 2021, 14(8), 239; https://doi.org/10.3390/a14080239 - 13 Aug 2021

Cited by 2 | Viewed by 2363

Abstract

Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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17 pages, 2427 KiB

Open AccessArticle

Optimization of the Weighted Multi-Facility Location Problem Using MS Excel

by Petr Němec, Petr Stodola, Miroslav Pecina, Jiří Neubauer and Martin Blaha

Algorithms 2021, 14(7), 191; https://doi.org/10.3390/a14070191 - 25 Jun 2021

Cited by 5 | Viewed by 3287

Abstract

This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software—MS Excel with the Solver add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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25 pages, 973 KiB

Open AccessArticle

A Primal-Dual Interior-Point Method for Facility Layout Problem with Relative-Positioning Constraints

by Shunichi Ohmori and Kazuho Yoshimoto

Algorithms 2021, 14(2), 60; https://doi.org/10.3390/a14020060 - 13 Feb 2021

Cited by 5 | Viewed by 3121

Abstract

We consider the facility layout problem (FLP) in which we find the arrangements of departments with the smallest material handling cost that can be expressed as the product of distance times flows between departments. It is known that FLP can be formulated as a linear programming problem if the relative positioning of departments is specified, and, thus, can be solved to optimality. In this paper, we describe a custom interior-point algorithm for solving FLP with relative positioning constraints (FLPRC) that is much faster than the standard methods used in the general-purpose solver. We build a compact formation of FLPRC and its duals, which enables us to establish the optimal condition very quickly. We use this optimality condition to implement the primal-dual interior-point method with an efficient Newton step computation that exploit special structure of a Hessian. We confirm effectiveness of our proposed model through applications to several well-known benchmark data sets. Our algorithm shows much faster speed for finding the optimal solution. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms and Applications)

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