Xin Liu¹, Jie Li^1,*, Jianwei Zhao², Bin Cao^2,*, Rongge Yan³, Zhihan Lyu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 143-185, 2024, DOI:10.32604/cmes.2023.030391

Abstract Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the… More > Graphic Abstract

Yadian Geng¹, Junqing Li^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 241-266, 2023, DOI:10.32604/cmes.2022.020307

Abstract To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on… More >

Muhammad Abid Jamil^*, Mohamed K. Nour

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 735-747, 2022, DOI:10.32604/cmc.2022.028386

Abstract Technical debt (TD) happens when project teams carry out technical decisions in favor of a short-term goal(s) in their projects, whether deliberately or unknowingly. TD must be properly managed to guarantee that its negative implications do not outweigh its advantages. A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden. Test technical debt is the technical debt aspect of testing (or test debt). Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years. In this article, we assume that the… More >

Shengzhong Li¹, Feng Zhao¹, Qi-Jun Ni¹

CMES-Computer Modeling in Engineering & Sciences, Vol.92, No.2, pp. 123-149, 2013, DOI:10.3970/cmes.2013.092.123

Abstract With the rapid development of computer technology and the continuous improvement of optimization theory, optimization techniques have been introduced into the field of ship design. Optimization algorithms and advanced CFD techniques are successfully integrated together into what is known as Simulation- Based Design (SBD) techniques, which opens a new situation for hull-form optimization design and configuration innovation. In this paper, fundamental elements of the SBD techniques are described and crucial components are analyzed profoundly. Focus is on breaking through key technologies as hull geometry modification and reconstruction, global optimization algorithms, and codes integration. Combined with high-fidelity CFD codes (on RANS),… More >

Fangyi Li^1,2, Zhen Luo³, Guangyong Sun⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.74, No.1, pp. 39-64, 2011, DOI:10.3970/cmes.2011.074.039

Abstract This paper studies the reliability-based multiobjective optimization by using a new interval strategy to model uncertain parameters. A new satisfaction degree of interval, which is significantly extended from [0, 1] to [–∞, +∞], is introduced into the non-probabilistic reliability-based optimization. Based on a predefined satisfaction degree level, the uncertain constraints can be effectively transformed into deterministic ones. The interval number programming method is applied to change each uncertain objective function to a deterministic two-objective optimization. So in this way the uncertain multiobjective optimization problem is transformed into a deterministic optimization problem and a reliability-based multiobjective optimization is then established. For… More >