Smart Computing, Optimization and Operations Research

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 2409

Special Issue Editors

Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul 151-744, Republic of Korea
Interests: transportation engineering; ship energy efficiency management; mathematical modeling; MCDM; machine learning
Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Augusto Rodin 498, Ciudad de Mexico 03920, Mexico
Interests: large-scale mathematical optimization; evolutionary calculus; statistical learning; computational intelligence; mathematical finance; health economics; energy economics; competition; economic regulations
Special Issues, Collections and Topics in MDPI journals
Graduate Program in Systems Engineering, Nuevo Leon State University (UANL), Av. Universidad s/n, Col. Ciudad Universitaria, San Nicolas de los Garza 66455, Nuevo Leon, Mexico
Interests: modeling, optimization and control of large scale systems; optimization; operations research
Special Issues, Collections and Topics in MDPI journals
UOW Malaysia, KDU Penang University College, George Town, Malaysia
Interests: intelligent systems techniques; deep learning algorithms; data science; visual analytics; scheduling and timetabling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are very happy to announce that the 6th International Conference on Intelligent Computing and Optimization 2023 will be held on 27–28 April 2023 at G Hua Hin Resort and Mall, Hua Hin, Thailand (Onsite Event) (https://www.icico.info/).

The International Conference on Intelligent Computing and Optimization (ICO’2023) highlights the latest research innovations and applications of algorithms designed for optimization applications within the fields of Science, Computer Science, Engineering, Information Technology, Management, Finance, Business and Economics.

With this conference in mind, we would like to invite authors to submit an extended version of their most successful papers to this Special Issue. Topics to be discussed at this conference include (but are not limited to) the following: 

  • Intelligent Computing: artificial intelligence, quantum computing, deep neural networks, self-organizing, fuzzy logic, membrane computing, evolutionary computation, learning theory, probabilistic methods, image processing computer vision, speech recognition, big data analytics, evolutionary algorithm, randomness Monte Carlo methods, algorithmic probability, chaos theory, cryptography, game theory, information theory, pattern recognition, natural computing, evolutionary robotics knowledge-based systems, machine learning, unsupervised learning, computational finance, computational economics, DNA computing, deep learning, wavelets, convolutional neural network, cloud computing, green computing. 
  • Optimization: ant colony optimization, artificial bee colony, artificial immune systems, artificial neural networks, automatic computing, bacterial foraging, biological computing, chaos optimization, cloud computing, combinatorial optimization, computational intelligence, continuous optimization, cultural algorithms, differential evolution, direct search, evolutionary computing, fuzzy optimization, genetic algorithms, granular computing, hybrid algorithms, local and global search, memetic algorithms, meta-heuristic methods, particle swarm optimization, pattern search, simulated annealing, simulation and modeling, soft computing techniques, support vector machines, swarm intelligence, tabu search, variable neighborhood search, quasi-Newton methods, monkey algorithm, mathematical programming. 
  • And their applications in science, technology, and engineering.

Dr. Tien Anh Tran
Prof. Dr. Roman Rodriguez Aguilar
Prof. Dr. Gerhard-Wilhelm Weber
Prof. Dr. Igor S. Litvinchev
Dr. Joshua Thomas
Dr. Pandian Vasant
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • smart computing
  • green computing
  • mathematical programming
  • deep neural network
  • data science
  • evolutionary computing
  • intelligent algorithms
  • mathematical optimization
  • optimization theories and methods
  • deep learning
  • neural networks
  • decision theory
  • big data analytics
  • modeling
  • operations research
  • stochastic optimization
  • linear and non-linear programming
  • combinatorial optimization

Published Papers (3 papers)

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Research

35 pages, 3767 KiB  
Article
A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems
Mathematics 2024, 12(3), 435; https://doi.org/10.3390/math12030435 - 29 Jan 2024
Viewed by 509
Abstract
The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example [...] Read more.
The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example chasing strategy is proposed to eliminate the destruction of the inferior solutions to superior solutions. Secondly, the heuristic crossover strategy is designed to enhance the search ability of the population. Finally, the prey search–attack strategy is used to balance the exploration and exploitation. To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. It concludes that MSBBO significantly outperforms all competitors both on convergence accuracy, speed and stability, and MSBBO basically converges to the same results on 10,000 dimensions as on 1000 dimensions. Further, MSBBO is applied to six real-world engineering design problems. The experimental results show that our work is still more competitive than other latest optimization techniques (COA, EDO, OMA, SHO and SCSO) on constrained optimization problems. Full article
(This article belongs to the Special Issue Smart Computing, Optimization and Operations Research)
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26 pages, 3248 KiB  
Article
Application of the Improved Cuckoo Algorithm in Differential Equations
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Mathematics 2024, 12(2), 345; https://doi.org/10.3390/math12020345 - 21 Jan 2024
Viewed by 538
Abstract
To address the drawbacks of the slow convergence speed and lack of individual information exchange in the cuckoo search (CS) algorithm, this study proposes an improved cuckoo search algorithm based on a sharing mechanism (ICSABOSM). The enhanced algorithm reinforces information sharing among individuals [...] Read more.
To address the drawbacks of the slow convergence speed and lack of individual information exchange in the cuckoo search (CS) algorithm, this study proposes an improved cuckoo search algorithm based on a sharing mechanism (ICSABOSM). The enhanced algorithm reinforces information sharing among individuals through the utilization of a sharing mechanism. Additionally, new search strategies are introduced in both the global and local searches of the CS. The results from numerical experiments on four standard test functions indicate that the improved algorithm outperforms the original CS in terms of search capability and performance. Building upon the improved algorithm, this paper introduces a numerical solution approach for differential equations involving the coupling of function approximation and intelligent algorithms. By constructing an approximate function using Fourier series to satisfy the conditions of the given differential equation and boundary conditions with minimal error, the proposed method minimizes errors while satisfying the differential equation and boundary conditions. The problem of solving the differential equation is then transformed into an optimization problem with the coefficients of the approximate function as variables. Furthermore, the improved cuckoo search algorithm is employed to solve this optimization problem. The specific steps of applying the improved algorithm to solve differential equations are illustrated through examples. The research outcomes broaden the application scope of the cuckoo optimization algorithm and provide a new perspective for solving differential equations. Full article
(This article belongs to the Special Issue Smart Computing, Optimization and Operations Research)
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18 pages, 3315 KiB  
Article
A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm
Mathematics 2024, 12(2), 186; https://doi.org/10.3390/math12020186 - 06 Jan 2024
Viewed by 590
Abstract
The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization [...] Read more.
The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software. Full article
(This article belongs to the Special Issue Smart Computing, Optimization and Operations Research)
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