Article
Computer Science, Theory & Methods
Kate Smith-Miles, Mario Andres Munoz
Summary: Instance Space Analysis (ISA) is a methodology developed to support objective testing of algorithms and assess the diversity of test instances. It enables visualization of the entire space of possible test instances and provides insights into how algorithm performance is affected by instance properties. This article serves as a comprehensive tutorial on the ISA methodology, including details of algorithms and software tools. A case study on university timetabling is presented to illustrate the methodology and tools.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Mohamed Abd Elaziz, Ahmed A. Ewees, Nabil Neggaz, Rehab Ali Ibrahim, Mohammed A. A. Al-qaness, Songfeng Lu
Summary: This paper introduces an alternative global optimization meta-heuristics approach inspired by natural selection theory, using competition among six algorithms to generate offspring. The proposed method shows efficiency compared to other well-known meta-heuristics methods. Variants of the method update individuals using different strategies, leading to successful outcomes.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Vinicius Renan de Carvalho, Ender Oezcan, Jaime Simao Sichman
Summary: This study investigates the performance of four state-of-the-art online hyper-heuristics with different characteristics in solving real-world multi-objective optimization problems. The results indicate that hyper-heuristics exhibit better cross-domain performance than single meta-heuristics, making them excellent candidates for solving new multi-objective optimization problems.
APPLIED SCIENCES-BASEL
(2021)
Article
Green & Sustainable Science & Technology
Tamas Banyai, Peter Veres
Summary: This study focuses on optimizing blending technologies by considering logistics parameters and using mathematical models and solution algorithms. The research aims to improve the supply chain processes of blending technologies.
Article
Engineering, Industrial
Hossam A. Kishawy, Amr Salem, Hussien Hegab, Ali Hosseini, Marek Balazinski
Summary: The implementation of micro-textured cutting tools is an effective strategy to improve dry machining processes by reducing friction and heat for better surface quality and longer tool life. However, derivative cutting, a result of micro-cutting on the bottom side of the chip, can increase cutting forces, heat, and tool wear. Further research is needed to address the issue of derivative cutting and optimize micro-texture design parameters.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Xiaofei Wang, Jiazhong Xu, Cheng Huang
Summary: This paper proposes the Fans Optimization (FO) algorithm, inspired by the Fans economy in the entertainment domain, for optimizing practical engineering problems. The FO algorithm introduces a Multi-groups mechanism and a Two-characteristic individual update mechanism to balance exploration and exploitation, showing superior performance in convergence and E&E compared to 12 other meta-heuristic algorithms. The FO algorithm is also proven to be superior to competitors through experiments and tests on benchmark functions, practical engineering problems, and the inverse kinematic solution problem of a 9-degree of freedom serial robot arm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Mechanical
Ruixiang Zheng, Yijie Wang, Mian Li
Summary: Multidisciplinary design optimization (MDO) has become crucial in dealing with the increased complexity of systems, involving multiple disciplines which make the problem complex and coupled. The Sequential Multidisciplinary Design Optimization (S-MDO) architecture offers a method to decompose the original MDO problem into subproblems efficiently. Theoretical analysis shows that the S-MDO architecture performs well as long as the global optimum of each disciplinary subproblem can be obtained.
JOURNAL OF MECHANICAL DESIGN
(2021)
Article
Computer Science, Artificial Intelligence
Michal Okulewicz, Mateusz Zaborski, Jacek Mandziuk
Summary: This paper introduces a new version of a hyper-heuristic framework called Generalized Self-Adapting Particle Swarm Optimization with samples archive (M-GAPSO). The framework hybridizes Particle Swarm Optimization, Differential Evolution, and model based optimizers and regulates the ratio of different algorithms within the population using an adaptation scheme. Experimental results on various benchmark functions demonstrate that M-GAPSO outperforms other optimization methods, including the basic DE algorithm.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Y-h. Taguchi, Turki Turki
Summary: Identifying differentially expressed genes is challenging due to limited sample size compared to the large number of genes. Statistical tests often have the problem of P-value dependence on sample size. This study explores the success of principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) by relating it to projection pursuit (PP). Additionally, empirical threshold-adjusted P-values are compared to assess the null hypothesis. The findings rationalize the success of PCA- and TD-based unsupervised FE for the first time.
Article
Chemistry, Multidisciplinary
Nicolas Alberto Sbrugnera Sotomayor, Fabrizia Caiazzo, Vittorio Alfieri
Summary: Additive manufacturing has revolutionized the production of complex lightweight designs in the past few decades, leading to a new era in the design process. It is crucial to consider multiple stages in the design for additive manufacturing (DfAM) process and implement guided-design frameworks to efficiently manage the process. This paper aims to minimize the number of design evaluations through optimization, design, and simulation tools, while focusing on implementing design optimization strategies to maximize additive manufacturing capabilities.
APPLIED SCIENCES-BASEL
(2021)
Article
Biology
Jeff Smith, R. Fredrik Inglis
Summary: Kin selection and multilevel selection theory are often used to interpret experiments about the evolution of cooperation and social behavior among microbes, but they are mostly used as conceptual heuristics. This study evaluates how these theories perform as quantitative analysis tools, finding that the classical fitness models of both theories are often unsuitable for microbial systems due to strong selection and non-additive effects. Analyzing both individual and group fitness outcomes can help clarify the biology of selection and reveal untapped potential for understanding social evolution in all branches of life.
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2021)
Article
Green & Sustainable Science & Technology
Yuan Liu, Heshan Zhang, Tao Xu, Yaping Chen
Summary: This study proposes a simultaneous optimization model that considers flow assignment and vehicle capacity for transit network design, aiming to reduce total travel time and the number of transfers. It develops a heuristic algorithm to generate initial routes and allocate vehicles to each route based on flow share. The concept of vehicle difference is introduced to evaluate the distinction between actual allocated vehicles and required vehicles for each route. The optimization process based on vehicle difference ensures that the solution meets the constraints.
Article
Engineering, Chemical
Andre L. M. Nahes, Miguel J. Bagajewicz, Andre L. H. Costa
Summary: The existing methods for optimizing the design of heat exchangers have limitations, with errors in describing the behavior of the equipment. To address this issue, a new integer linear model was developed for optimizing the design of hairpin double-pipe heat exchangers, with numerical results demonstrating its performance advantages.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Sumit Kumar, Natee Panagant, Ghanshyam G. Tejani, Nantiwat Pholdee, Sujin Bureerat, Nikunj Mashru, Pinank Patel
Summary: Multi-objective structure optimization is a complex design issue that involves dealing with multiple conflicting objectives and various constraints. A powerful optimizer called MOMVO2arc has been proposed and evaluated for solving large structure optimization problems with less computation time.
KNOWLEDGE-BASED SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Esther Omolara Abiodun, Abdulatif Alabdulatif, Oludare Isaac Abiodun, Moatsum Alawida, Abdullah Alabdulatif, Rami S. Alkhawaldeh
Summary: Data preparation techniques such as feature selection are crucial for optimizing predictive models for classification tasks. Traditional feature selection methods may not effectively reduce high dimensionality in text data, but emerging technologies like metaheuristics and hyper-heuristics optimization methods offer new possibilities for improving model accuracy and efficiency. Despite the potential benefits, there is still a need for best practices in utilizing these emerging feature selection methods for text classification tasks.
NEURAL COMPUTING & APPLICATIONS
(2021)
