Random Mutation Strategy Research Articles

An improved RIME optimization algorithm for lung cancer image segmentation

Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.

A multi-strategy improved tree–seed algorithm for numerical optimization and engineering optimization problems

Tree–seed algorithm is a stochastic search algorithm with superior performance suitable for solving continuous optimization problems. However, it is also prone to fall into local optimum and slow in convergence. Therefore, this paper proposes an improved tree–seed algorithm based on pattern search, dimension permutation, and elimination update mechanism (PDSTSA). Firstly, a global optimization strategy based on pattern search is used to promote detection ability. Secondly, in order to maintain the diversity of the population, a random mutation strategy of individual dimension replacement is introduced. Finally, the elimination and update mechanism based on inferior trees is introduced in the middle and later stages of the iteration. Subsequently, PDSTSA is compared with seven representative algorithms on the IEEE CEC2015 test function for simulation experiments and convergence curve analysis. The experimental results indicate that PDSTSA has better optimization accuracy and convergence speed than other comparison algorithms. Then, the Wilcoxon rank sum test demonstrates that there is a significant difference between the optimization results of PDSTSA and each comparison algorithm. In addition, the results of eight algorithms for solving engineering constrained optimization problems further prove the feasibility, practicability, and superiority of PDSTSA.

Structural energy transfer to the jacket foundation of an offshore wind-wave coupling device based on absolute transmissibility function

During the long-term operation of an offshore fixed wind-wave coupling device, the structure is subjected to excitation due to the operation of the wave energy device, harmonic excitation due to the impeller rotation, and environmental excitation. In this study, the whale optimization algorithm (WOA) modified by a random mutation strategy was used to optimize the variational mode decomposition (VMD) parameters and eliminate the effect of environmental noise. Then, the normalization transfer path analysis (N-TPA) method based on the absolute transmissibility function (ATF) was used to analyze the transfer path from each vibration source to the target point. The energy transfer among the structures of the wind-wave coupling device was studied; accordingly, the contribution of the subsystems was intuitively displayed to determine the pile with the highest energy when subjected to external loads. Compared to the traditional transfer path analysis (TPA) method based on the transmissibility function (TF), the method developed in this study can overcome the ocean noise problem through the improved WOA-VMD method and yield more accurate transfer path analysis results. To verify the effectiveness of the method developed in this study, numerical research, simulation, and physical model experiments were conducted using the 5-DOF system theoretical model, finite element model, and physical model. The contributions of the subsystems to the operational state of the structure were analyzed. The results demonstrated that the proposed method could be used to evaluate the stability of each structural element such that appropriate measures can be taken. This provides a reference for the development and stable operation of fixed wind-wave coupling devices.

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set determines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimization. However, different algorithms have different performances for different medical disease images. For example, the Whale Optimization Algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

l The resource scheduling model of intelligent manufacturing workshop is established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. l The NSGA-Ⅱ algorithm is improved, and the evaluation function is established based on crowding ranking and ranking level. l the competition mechanism is introduced, the elitist retention strategy is improved, the probability is determined by variable proportion method, and the optimal solution is determined by AHP. With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

Construction and Characterization of Broad-Spectrum Promoters for Synthetic Biology.

Characterization of genetic circuits and biosynthetic pathways in different hosts always requires promoter substitution and redesigning. Here, a strong, broad-spectrum promoter, Pbs, for Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae was constructed, and it was incorporated into the minimal E.coli-B.subtilis-S.cerevisiae shuttle plasmid pEBS (5.8 kb). By applying a random mutation strategy, three broad-spectrum promoters Pbs1, Pbs2, and Pbs3, with different strengths were generated and characterized. These broad-spectrum promoters will expand the synthetic biology toolbox for E.coli, B.subtilis, and S.cerevisiae.

A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R2) of the whole training set (training and prediction) was 0.924, and the R2 of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q2 value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.

A New Variant Harmony Search Algorithm with Random Mutation Strategy

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method, which has been paid much attention recently. However, intelligent optimization methods are easily trapped into local optima, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm with random mutation strategy (HSRM) is proposed in this paper. The HSRM uses a random mutation strategy to replace the pitch adjusting operation, and dynamically adjust the key parameter pitch adjusting rate (PAR). Experiment results demonstrated that the proposed method is superior to the HS and recently developed variants (IHS, and GHS) and other meta-heuristic algorithm.

Improvement of plasmid encoded lactococcal bacteriophage resistance by mutator strain Epicurian coli XL1-Red

A random mutation strategy using mutator strain, Epicurian coli XL1-Red, was applied to a plasmid, pND018, constructed by inserting a Lactococcus lacis bacteriophage resistance gene (abiI) into a L. lactis/E. coli shuttle vector (pDL278), to introduce random mutations throughout the plasmid. Following transformation of the mutated plasmid library to a plasmid free and phage sensitive strain of L. lactis (LM0230), mutated plasmids were screened by cross-streaking and efficiency of plaquing (EOP) assays. Two strains with enhanced resistance were obtained, as well as several phage sensitive strains. Repeated transformation of the mutated plasmids to LM0230 confirmed that the observed phenotypes were caused by mutations located on the plasmids. The EOP values and plaque morphology of two enhanced phage resistance mutants were characterized at 30 °C and 37 °C. These results indicate that this simple procedure can be applied to generate modified plasmids with improved phage resistance, which may be of commercial value.