Multi-scale topology optimization (MTO) is exploited today in applications that require designs with large surface-to-volume ratio. Further, the advent of additive manufacturing, MTO has gained significant prominence. However, a major drawback it computationally expensive. As an alternate, graded been proposed where design features at smaller scale are variations single microstructure. This lea...

With the continuous improvement of deep object detectors via advanced model architectures, imbalance problems in training process have received more attention. It is a common paradigm detection frameworks to perform multi-scale detection. However, each scale treated equally during training. In this paper, we carefully study objective detector We argue that loss level neither important nor indep...

Abstract The main objective of this work is to extend finite element-based topology optimization problem the two-dimensional, size-dependent structures described using weakly non-local Cosserat (micropolar) and strongly Eringen’s theories, latter which finds an application for first time, best Authors’ knowledge. optimum material layouts that minimize structural compliance are attained by means...

background and objective: lactobacillus plantarum is one of the probiotics species used in functional food products. these bacteria or their purified bacteriocins are used as biological preservatives in the food industry. the first step in production of an array of probiotic products is optimizing production in fermentors. this study aimed to examine factors affecting the in vitro growth optimi...

The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, paper proposes self-organizing weighted based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original there are two main improvements our work. Firstly, S-WOF simplifies stage into one stage, which evaluating numbers...

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.