Special Issue "Numerical and Evolutionary Optimization"

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Dr. Adriana Lara

Instituto Politécnico Nacional ESFM-IPN, 07730 Mexico City, Mexico
E-Mail
Interests: multi-objective optimization; optimization; evolutionary computation; mathematical programming; memetic algorithms
Guest Editor
Dr. Marcela Quiroz

Centro de Investigación en Inteligencia Artificial, University of Veracruz, 91000 Xalapa, Mexico
E-Mail
Interests: experimental algorithmics; metaheuristics; genetic algorithms; bin packing; machine learning; causal inference applications
Guest Editor
Dr. Efrén Mezura-Montes

Centro de Investigación en Inteligencia Artificial, University of Veracruz, 91000 Xalapa, Mexico
Website | E-Mail
Interests: evolutionary computation; global optimization; multiobjective optimization; constraint-handling
Guest Editor
Prof. Dr. Oliver Schütze

Depto de Computacion, CINVESTAV-IPN, 07360 Mexico City, Mexico
Website | E-Mail
Interests: multi-objective optimization; evolutionary computation (genetic algorithms and evolution strategies); numerical analysis; engineering applications

Special Issue Information

Dear Colleagues,

The development of powerful search and optimization techniques is of great importance in science and engineering, particularly in today's world, which requires researchers and practitioners to tackle a variety of challenging real-world problems as technology becomes an ever-more-important aspect of everyday life. There are two well-established and widely-known fields that are addressing these issues: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics, such as evolutionary algorithms and genetic programming. Both of these fields have developed approaches with their unique strengths and weaknesses, allowing them to solve some challenging problems while sometimes failing in others.

Recent studies have shown that the consideration of elements coming from both fields can lead to great synergies, e.g., in understanding of certain algorithms or in the design of new search techniques.

The aim of this Special Issue is to collect papers on the intersection of numerical and evolutionary optimization. We strongly encourage the development of fast and reliable hybrid methods, that maximize the strengths and minimize the weaknesses of each underlying paradigm, while also being applicable to a broader class of problems. Moreover, this Special Issue fosters the understanding and adequate treatment of real-world problems, particularly in emerging fields that affect us all, such as health care, smart cities, and big data, among many others.

Topics of interest include (but are not limited to):


(A) Search and Optimization:

  • Single- and multi-objective optimization
  • Advances in evolutionary algorithms and genetic programming
  • Hybrid and memetic algorithms
  • Set oriented numerics
  • Stochastic optimization
  • Robust optimization

(B) Real World Problems:

  • Health systems
  • Computer vision and pattern recognition
  • Energy conservation and prediction
  • Modeling and control of real-world systems
  • Smart cities

Dr. Adriana Lara
Dr. Marcela Quiroz
Dr. Efrén Mezura-Montes
Prof. Dr. Oliver Schütze
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 papers will be 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. Mathematical and Computational Applications is an international peer-reviewed open access quarterly 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 300 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.

Published Papers (5 papers)

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Research

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Open AccessArticle An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand
Math. Comput. Appl. 2018, 23(3), 40; https://doi.org/10.3390/mca23030040
Received: 14 July 2018 / Revised: 5 August 2018 / Accepted: 9 August 2018 / Published: 10 August 2018
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Abstract
This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving
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This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving methods are presented: (1) developing a mathematical model and solving it using Lingo v.11, and (2) using three improved Differential Evolution (DE) Algorithms—I-DE-SW, I-DE-CY, and I-DE-KV—which are DE with swap, cyclic moves (CY), and K-variables moves (KV) respectively. The algorithms were tested by 16 test instances, including this case study. The computational results showed that Lingo v.11 and all DE algorithms can find the optimal solution eight out of 16 times. Regarding the remaining test instances, Lingo v.11 was unable to find the optimal solution within 400 h. The results for the DE algorithms were compared with the best solution generated within that time. The DE solutions were 1.196–1.488% better than the best solution generated by Lingo v.11 and used 200 times less computational time. Comparing the three DE algorithms, MDE-KV was the DE that was the most flexible, with the biggest neighborhood structure, and outperformed the other DE algorithms. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
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Open AccessArticle Modified Differential Evolution Algorithm Solving the Special Case of Location Routing Problem
Math. Comput. Appl. 2018, 23(3), 34; https://doi.org/10.3390/mca23030034
Received: 18 June 2018 / Revised: 1 July 2018 / Accepted: 1 July 2018 / Published: 3 July 2018
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Abstract
This research article aims to solve the special case of the location routing problem (SLRP) when the objective function is the fuel consumption. The fuel consumption depends on the distance of travel and the condition of the road. The condition of the road
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This research article aims to solve the special case of the location routing problem (SLRP) when the objective function is the fuel consumption. The fuel consumption depends on the distance of travel and the condition of the road. The condition of the road causes the vehicle to use a different speed, which affects fuel usage. This turns the original LRP into a more difficult problem. Moreover, the volume of the goods that are produced in each node could be more or less than the capacity of the vehicle, and as the case study requires the transportation of latex, which is a sensitive good and needs to be carried within a reasonable time so that it does not form solid before being used in the latex process, the maximum time that the latex can be in the truck is limited. All of these attributes are added into the LRP and make it a special case of LRP: a so-called SLRP (a special case of location routing problem). The differential evolution algorithms (DE) are proposed to solve the SLRP. We modified two points in the original DE, which are that (1) the mutation formula is introduced and (2) the new rule of a local search is presented. We call this the modified differential evolution algorithm (MDE). From the computational result, we can see that MDE generates a 13.82% better solution than that of the original version of DE in solving the test instances. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
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Open AccessArticle The Construction of a Model-Robust IV-Optimal Mixture Designs Using a Genetic Algorithm
Math. Comput. Appl. 2018, 23(2), 25; https://doi.org/10.3390/mca23020025
Received: 10 April 2018 / Revised: 7 May 2018 / Accepted: 16 May 2018 / Published: 17 May 2018
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Abstract
Among the numerous alphabetical optimality criteria is the IV-criterion that is focused on prediction variance. We propose a new criterion, called the weighted IV-optimality. It is similar to IV-optimality, because the researcher must first specify a model. However, unlike IV-optimality, a suite of
[...] Read more.
Among the numerous alphabetical optimality criteria is the IV-criterion that is focused on prediction variance. We propose a new criterion, called the weighted IV-optimality. It is similar to IV-optimality, because the researcher must first specify a model. However, unlike IV-optimality, a suite of “reduced” models is also proposed if the original model is misspecified via over-parameterization. In this research, weighted IV-optimality is applied to mixture experiments with a set of prior weights assigned to the potential mixture models of interest. To address the issue of implementation, a genetic algorithm was developed to generate weighted IV-optimal mixture designs that are robust across multiple models. In our examples, we assign models with p parameters to have equal weights, but weights will vary based on varying p. Fraction-of-design-space (FDS) plots are used to compare the performance of an experimental design in terms of the prediction variance properties. An illustrating example is presented. The result shows that the GA-generated designs studied are robust across a set of potential mixture models. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
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Open AccessFeature PaperArticle How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior
Math. Comput. Appl. 2018, 23(2), 19; https://doi.org/10.3390/mca23020019
Received: 7 March 2018 / Revised: 27 March 2018 / Accepted: 30 March 2018 / Published: 3 April 2018
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Abstract
Road traffic injuries are a serious concern in emerging economies. Their death toll and economic impact are shocking, with 9 out of 10 deaths occurring in low or middle-income countries; and road traffic crashes representing 3% of their gross domestic product. One way
[...] Read more.
Road traffic injuries are a serious concern in emerging economies. Their death toll and economic impact are shocking, with 9 out of 10 deaths occurring in low or middle-income countries; and road traffic crashes representing 3% of their gross domestic product. One way to mitigate these issues is to develop technology to effectively assist the driver, perhaps making him more aware about how her (his) decisions influence safety. Following this idea, in this paper we evaluate computational models that can score the behavior of a driver based on a risky-safety scale. Potential applications of these models include car rental agencies, insurance companies or transportation service providers. In a previous work, we showed that Genetic Programming (GP) was a successful methodology to evolve mathematical functions with the ability to learn how people subjectively score a road trip. The input to this model was a vector of frequencies of risky maneuvers, which were supposed to be detected in a sensor layer. Moreover, GP was shown, even with statistical significance, to be better than six other Machine Learning strategies, including Neural Networks, Support Vector Regression and a Fuzzy Inference system, among others. A pending task, since then, was to evaluate if a more detailed comparison of different strategies based on GP could improve upon the best GP model. In this work, we evaluate, side by side, scoring functions evolved by three different variants of GP. In the end, the results suggest that two of these strategies are very competitive in terms of accuracy and simplicity, both generating models that could be implemented in current technology that seeks to assist the driver in real-world scenarios. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
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Review

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Open AccessFeature PaperReview A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction
Math. Comput. Appl. 2018, 23(2), 30; https://doi.org/10.3390/mca23020030
Received: 15 May 2018 / Revised: 25 May 2018 / Accepted: 31 May 2018 / Published: 1 June 2018
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Abstract
Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives.
[...] Read more.
Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control, which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort. Full article
(This article belongs to the Special Issue Numerical and Evolutionary Optimization)
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