UNASSIGNED: Improvement on the updating equation of an algorithm is among the most improving techniques. Due to the lack of search ability, high computational complexity and poor operability of equilibrium optimizer (EO) in solving complex optimization problems, an improved EO is proposed in this article, namely the multi-strategy on updating synthetized EO (MS-EO).
UNASSIGNED: Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages.
UNASSIGNED: Experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability. The experimental results of feature selection on several datasets show that MS-EO also provides more advantages.

2-KGA, a precursor for the synthesis of Vitamin C, is currently produced in China utilizing the \"two-step fermentation\" technique. Nevertheless, this method exhibits many inherent constraints. This study presents a comprehensive metabolic engineering strategy to establish and optimize a one-step 2-KGA fermentation process from D-sorbitol in Pseudomonas putida KT2440. In general, the endogenous promoters were screened to identify promoter P1 for subsequent heterologous gene expression in KT2440. Following the screening and confirmation of suitable heterologous gene elements such as sldh, sdh, cytc551, pqqAB, and irrE, genetic recombination was performed in KT2440. In comparison to the initial achievement of expressing only sldh and sdh in KT2440, a yield of merely 0.42 g/L was obtained. However, by implementing four metabolic engineering strategies, the recombinant strain KT20 exhibited a significant enhancement in its ability to produce 2-KGA with a remarkable yield of up to 6.5 g/L - representing an impressive 15.48-fold improvement.

In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency.

The real-time location of pollution sources is the process of inverting pollution sources based on the dynamic optimization model constructed by the time-varying pollution concentration detected by the water quality sensor. Due to the vast quantities of the water supply networks, the water quality sensors will only be placed on critical nodes, resulting in multiple solutions. However, the increased monitoring data enhances the uniqueness of the solution. Combined with the real-time location of pollution sources, this work proposed a multi-strategy dynamic multi-mode optimization algorithm based on domain knowledge, which could guide the population search and avoid trapped into local optimal. The merging mechanism was used to keep the diversity of the population and prevent sub-population clustering on the same optimal solution. The simulation results showed that the algorithm could effectively solve the real-time location problem of pollution sources in different pipe networks and pollution scenarios.

Nicotinamide adenine dinucleotide (NAD+) is an essential coenzyme involved in numerous physiological processes. As an attractive product in the industrial field, NAD+ also plays an important role in oxidoreductase-catalyzed reactions, drug synthesis, and the treatment of diseases, such as dementia, diabetes, and vascular dysfunction. Currently, although the biotechnology to construct NAD+-overproducing strains has been developed, limited regulation and low productivity still hamper its use on large scales. Here, we describe multi-strategy metabolic engineering to address the NAD+-production bottleneck in E. coli. First, blocking the degradation pathway of NAD(H) increased the accumulation of NAD+ by 39%. Second, key enzymes involved in the Preiss-Handler pathway of NAD+ synthesis were overexpressed and led to a 221% increase in the NAD+ concentration. Third, the PRPP synthesis module and Preiss-Handler pathway were combined to strengthen the precursors supply, which resulted in enhancement of NAD+ content by 520%. Fourth, increasing the ATP content led to an increase in the concentration of NAD+ by 170%. Finally, with the combination of all above strategies, a strain with a high yield of NAD+ was constructed, with the intracellular NAD+ concentration reaching 26.9 μmol/g DCW, which was 834% that of the parent strain. This study presents an efficient design of an NAD+-producing strain through global regulation metabolic engineering.