Metabolic pathway design is crucial in synthetic biology. Compared to traditional manual methods, evolutionary computation-based approaches greatly reduce time and cost, becoming the dominant trend. Current algorithms fall into two categories: single-objective and multi-objective. Multi-objective methods outperform single-objective ones in diversity and convergence, making them better suited for complex problems by balancing multiple performance metrics. However, existing multi-objective evolutionary algorithms for pathway design remain inefficient. To address this, we propose an efficient method based on a multi-objective shuffled frog leaping algorithm. Experiments show our approach significantly reduces computation time and discovers higher-performing pathways compared to existing methods.

As the resource density of cloud servers increases, cloud providers deploy hundreds of VMs concurrently on a single server, requiring a high-performance, scalable, flexible and high-density I/O virtualization method. Hardware assisted virtualization such as device pass-through with SR-IOV can achieve near-native performance, however, at the expense of flexibility and a limited device count. Traditional software-based I/O virtualization systems tend to dedicate additional computing cores for higher performance, but suffer from critical scalability problems especially in high-density cloud.

Recently, metabolic pathway design has attracted considerable attention and become an increasingly important area in metabolic engineering. Manual or computational methods have been introduced to retrieve the metabolic pathway. These methods model metabolic pathway design as a single-objective optimization problem with the weighted sum of a variety of criteria as the final score. While these methods have demonstrated promising results, the majority of current methods do not account for comparisons and competition among criteria. Here, we propose MooSeeker, a metabolic pathway design tool based on the multi-objective optimization algorithm that aims to trade off all the criteria optimally. The metabolic pathway design problem is characterized as a multi-objective optimization problem with three objectives including pathway length, thermodynamic feasibility and theoretical yield. In order to digitize the continuous metabolic pathway, MooSeeker develops the encoding strategy, BioCrossover and BioMutation operators to search for the candidate pathways. Finally, MooSeeker outputs the Pareto optimal solutions of the candidate metabolic pathways with three criterion values. The experiment results show that MooSeeker is capable of constructing the experimentally validated pathways and finding the higher-performance pathway than the single-objective-based methods.

Evolutionary algorithms (EAs) show strong adaptability in solving the complex optimization problems. Fitness evaluation is an important step of EA. In this step, the fitness of individuals is calculated by fitness evaluation methods. However, there are many complex optimization problems in which the existing fitness evaluation methods cannot express solutions’ fitness by crisp values but only fuzzy fitness information. Even if some fitness evaluation methods can predict crisp values to express the individuals’ fitness, these methods must “see” some crisp fitness information in advance. These shortcomings of existing fitness evaluation methods make EAs unable to work effectively in many optimization tasks. In this article, we propose the concept of blind fitness evaluation. A blind fitness evaluation method (BFEM) can output individuals’ crisp fitness information without “seeing” any crisp fitness information in advance. Based on the concept, we propose a BFEM which uses an artificial neural network, named modified fitness comparison network (MFCN) to evaluate the fitness of individuals. The MFCN can be trained by fuzzy fitness information, and the trained MFCN can predict the crisp fitness information of any individual. By applying the BFEM to EAs, the EAs with blind fitness evaluation are obtained and the algorithms can effectively solve the complex optimization problems with only fuzzy fitness information. The proposed BFEM is compared with several popular fitness evaluation methods that rely on crisp fitness information. Experimental results show that the fitness evaluation accuracy of MFCN trained based on only fuzzy fitness information is similar to or higher than that of the fitness evaluation methods relying on crisp fitness information. The EAs with MFCN also show higher performance than the EAs with the popular fitness evaluation methods in search ability and solution quality.