Special Issue "Metaheuristic Algorithms in Optimization and Applications"

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

Deadline for manuscript submissions: closed (31 August 2016)

Special Issue Editors

Guest Editor
Dr. Yun-Chia Liang

Industrial Engineering and Management Department, Yuan Ze University, Taiwan
Website | E-Mail
Interests: combinatorial optimization; meta-heuristic; neural network; production scheduling; supply chain management
Guest Editor
Dr. Mehmet Fatih Tasgetiren

Industrial Engineering Department, Yasar University, Turkey
Website | E-Mail
Interests: heuristic optimization; scheduling; real parameter optimization
Guest Editor
Dr. Quan-Ke Pan

State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, China
Website | E-Mail
Interests: intelligent optimization theory, algorithms and applications modeling, optimization and scheduling for production manufacturing systems

Special Issue Information

Dear Colleagues,

Metaheuristic algorithms have attracted a great deal of attention in computer science, artificial intelligence, machine learning, engineering design, data mining, planning and scheduling, logistics and supply chains, etc. This Special Issue focuses on the recent developments of metaheuristic algorithms and their diverse applications, as well as theoretical studies.

We invite authors to contribute original research articles as well as review articles on recent advances in these active research areas. Topics of interest include, but are not limited to:

  • Swarm intelligence
  • Nature-inspired metaheuristic algorithms
  • Neighborhood search algorithms
  • New metaheuristics/operators
  • Parallelization of metaheuristics
  • Hybridized algorithms
  • Empirical and theoretical research of metaheuristics
  • High-impact applications of metaheuristics
  • Challenging problems such as multi-objective, stochastic, or dynamic problems

Dr. Yun-Chia Liang
Dr. Mehmet Fatih Tasgetiren
Dr. Quan-Ke Pan
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.

Keywords

  • Meta-heuristics
  • Optimization
  • Swarm Intelligence

Published Papers (10 papers)

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Research

Open AccessArticle Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm
Algorithms 2017, 10(1), 19; https://doi.org/10.3390/a10010019
Received: 24 November 2016 / Revised: 9 January 2017 / Accepted: 18 January 2017 / Published: 23 January 2017
Cited by 1 | PDF Full-text (2032 KB) | HTML Full-text | XML Full-text
Abstract
In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence
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In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions
Algorithms 2017, 10(1), 18; https://doi.org/10.3390/a10010018
Received: 28 September 2016 / Revised: 4 January 2017 / Accepted: 16 January 2017 / Published: 23 January 2017
Cited by 7 | PDF Full-text (4449 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it
[...] Read more.
In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization
Algorithms 2017, 10(1), 9; https://doi.org/10.3390/a10010009
Received: 27 November 2016 / Revised: 27 December 2016 / Accepted: 4 January 2017 / Published: 8 January 2017
Cited by 1 | PDF Full-text (3901 KB) | HTML Full-text | XML Full-text
Abstract
The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy
[...] Read more.
The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization
Algorithms 2016, 9(4), 78; https://doi.org/10.3390/a9040078
Received: 18 July 2016 / Revised: 2 November 2016 / Accepted: 14 November 2016 / Published: 18 November 2016
Cited by 1 | PDF Full-text (811 KB) | HTML Full-text | XML Full-text
Abstract
The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In
[...] Read more.
The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle A Variable Block Insertion Heuristic for the Blocking Flowshop Scheduling Problem with Total Flowtime Criterion
Algorithms 2016, 9(4), 71; https://doi.org/10.3390/a9040071
Received: 30 June 2016 / Revised: 1 October 2016 / Accepted: 8 October 2016 / Published: 20 October 2016
Cited by 7 | PDF Full-text (2499 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the
[...] Read more.
In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the VBIH algorithm starts with a minimum block size being equal to one. It removes the block from the current sequence and inserts it into the partial sequence sequentially with a predetermined move size. The sequence, which is obtained after several block moves, goes under a variable local search (VLS), which is based on traditional insertion and swap neighborhood structures. If the new sequence obtained after the VLS local search is better than the current sequence, it replaces the current sequence. As long as it improves, it keeps the same block size. However, if it does not improve, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the current sequence. This process is repeated until the block size reaches at the maximum block size. Furthermore, we present a novel constructive heuristic, which is based on the profile fitting heuristic from the literature. The proposed constructive heuristic is able to further improve the best known solutions for some larger instances in a few seconds. Parameters of the constructive heuristic and the VBIH algorithm are determined through a design of experiment approach. Extensive computational results on the Taillard’s well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm, which is one of the most efficient algorithms recently in the literature. Ultimately, 52 out of the 150 best known solutions are further improved with substantial margins. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
Algorithms 2016, 9(4), 69; https://doi.org/10.3390/a9040069
Received: 7 July 2016 / Revised: 16 September 2016 / Accepted: 30 September 2016 / Published: 14 October 2016
Cited by 10 | PDF Full-text (5155 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate)
[...] Read more.
In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate) parameters that improve the convergence rate of traditional harmony search algorithm (HS). The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle A Practical and Robust Execution Time-Frame Procedure for the Multi-Mode Resource-Constrained Project Scheduling Problem with Minimal and Maximal Time Lags
Algorithms 2016, 9(4), 63; https://doi.org/10.3390/a9040063
Received: 4 July 2016 / Revised: 6 September 2016 / Accepted: 16 September 2016 / Published: 24 September 2016
PDF Full-text (1284 KB) | HTML Full-text | XML Full-text
Abstract
Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP), improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future
[...] Read more.
Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP), improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max). The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements
Algorithms 2016, 9(3), 51; https://doi.org/10.3390/a9030051
Received: 26 April 2016 / Revised: 24 July 2016 / Accepted: 27 July 2016 / Published: 30 July 2016
Cited by 3 | PDF Full-text (6648 KB) | HTML Full-text | XML Full-text
Abstract
This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the
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This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems
Algorithms 2016, 9(3), 47; https://doi.org/10.3390/a9030047
Received: 27 April 2016 / Revised: 2 July 2016 / Accepted: 18 July 2016 / Published: 22 July 2016
Cited by 5 | PDF Full-text (2118 KB) | HTML Full-text | XML Full-text
Abstract
This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP)
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This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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Open AccessArticle Opposition-Based Adaptive Fireworks Algorithm
Algorithms 2016, 9(3), 43; https://doi.org/10.3390/a9030043
Received: 2 April 2016 / Revised: 13 June 2016 / Accepted: 4 July 2016 / Published: 8 July 2016
Cited by 3 | PDF Full-text (1081 KB) | HTML Full-text | XML Full-text
Abstract
A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to
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A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)
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