Special Issue "Evolutionary Computation for Multiobjective Optimization"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 December 2017)

Special Issue Editors

Guest Editor
Dr. Ngai Ming Kwok

School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
Website | E-Mail
Interests: Intelligent computation; Image processing; Non-linear modelling; Intelligent control
Guest Editor
Dr. Ying-Hao Yu

Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Min-Hsiung, Chiayi, Taiwan
Website | E-Mail
Interests: Computer Vision; Robotic Controls; Artificial Intelligence; System on Chip; Embedded System

Special Issue Information

Dear Colleagues,

The exploration of novel algorithms for multi-objective optimization is still an open issue in scientific fields. Although many successful cases have been reported in recent years through the use of evolutionary algorithms, the reliability and performance are highly dependent on the number of objectives. On the one hand, many evolutionary algorithms may achieve sufficient accuracy; on the other hand, the balance of complexity and real-time performance has the disadvantage of complicated computing processes. Notwithstanding these concerns, this kind of methodology is commonly applied in management, production quality control, and business strategy decision-making. In response to the latest trend in mobile platforms, this Special Issue stresses the practicality of evolutionary computing for multi-objective optimization on dynamic or embedded systems. Topics of interest include, but are not limited to, novel algorithms with sufficient reliability and real-time performance for application in the following areas:

Ambient intelligence
Biometrics authentication
Green energy
Image processing
Intelligent grid management
Intelligent transport system designs
Machine vision
Robotic control and learning

Dr. Ngai Ming  Kwok
Dr. Ying-Hao  Yu
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. 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 850 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

Open AccessArticle Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map
Algorithms 2018, 11(3), 30; https://doi.org/10.3390/a11030030
Received: 17 January 2018 / Revised: 6 March 2018 / Accepted: 14 March 2018 / Published: 15 March 2018
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Abstract
Cuckoo Search (CS) is a Meta-heuristic method, which exhibits several advantages such as easier to application and fewer tuning parameters. However, it has proven to very easily fall into local optimal solutions and has a slow rate of convergence. Therefore, we propose Modified
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Cuckoo Search (CS) is a Meta-heuristic method, which exhibits several advantages such as easier to application and fewer tuning parameters. However, it has proven to very easily fall into local optimal solutions and has a slow rate of convergence. Therefore, we propose Modified cuckoo search algorithm with variational parameter and logistic map (VLCS) to ameliorate these defects. To balance the exploitation and exploration of the VLCS algorithm, we not only use the coefficient function to change step size α and probability of detection p a at next generation, but also use logistic map of each dimension to initialize host nest location and update the location of host nest beyond the boundary. With fifteen benchmark functions, the simulations demonstrate that the VLCS algorithm can over come the disadvantages of the CS algorithm.In addition, the VLCS algorithm is good at dealing with high dimension problems and low dimension problems. Full article
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Open AccessArticle A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
Algorithms 2018, 11(2), 16; https://doi.org/10.3390/a11020016
Received: 28 November 2017 / Revised: 17 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
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Abstract
Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio
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Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO) from two aspects: first, we introduce differential evolution (DE) process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS) process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance. Full article
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Open AccessArticle 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization
Algorithms 2017, 10(4), 130; https://doi.org/10.3390/a10040130
Received: 17 October 2017 / Revised: 17 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
Cited by 1 | PDF Full-text (854 KB) | HTML Full-text | XML Full-text
Abstract
Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk
[...] Read more.
Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA). In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm. Full article
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Open AccessArticle A Selection Process for Genetic Algorithm Using Clustering Analysis
Algorithms 2017, 10(4), 123; https://doi.org/10.3390/a10040123
Received: 16 August 2017 / Revised: 30 October 2017 / Accepted: 31 October 2017 / Published: 2 November 2017
Cited by 1 | PDF Full-text (712 KB) | HTML Full-text | XML Full-text
Abstract
This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis
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This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGAf) and via an optimal partitioning Kopt (KGAo) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems. Full article
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Open AccessArticle An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
Algorithms 2017, 10(3), 86; https://doi.org/10.3390/a10030086
Received: 13 June 2017 / Revised: 18 July 2017 / Accepted: 24 July 2017 / Published: 26 July 2017
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Abstract
MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well.
[...] Read more.
MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4–6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious. Full article
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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