Discrete farmland fertility optimization algorithm with metropolis acceptance criterion for traveling salesman problems

Design optimization of office building envelope by developed farmland fertility algorithm for energy saving

This study focuses on designing sustainable buildings with a specific emphasis on reducing energy consumption and optimizing costs. To address the time-consuming nature of simulation software like TRNSYS and Energy Plus, a novel meta-heuristic optimization algorithm called the Developed Optimization Algorithm of Farmland Fertility (DFFA) is provided. The DFFA algorithm is utilized to optimize parameters related to the building envelope, encompassing walls, windows, and glass curtain walls, aiming to minimize energy demand and construction expenses. Comparative analysis with other approaches such as EPO, LOA, MVO, and FFA demonstrates significant reductions in energy consumption and building design costs achieved by employing the proposed algorithm. Furthermore, the DFFA algorithm yields the desired results within fewer iterations. By increasing the surface area of the glass curtain wall and total window space, improvements in natural ventilation and interior lighting are observed. Despite similar window opening measurements across the compared methods, the suggested algorithm surpasses others when it comes to overall cost and energy efficiency. The total cost reduction compared to the initial design amounts to 39 %. Thus, the DFFA algorithm proves to be more effective in conserving energy in buildings compared to other analyzed procedures. This research serves as a case study and presents a promising method applicable to designing various building types under different weather conditions in future projects.

Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery

BACKGROUND AND OBJECTIVE

Cancer subtype discovery is essential for personalized clinical treatment. With the onset of progressive profile techniques for cancer, a large amount of heterogeneous and high-dimensional transcriptomic, proteomic and genomic datasets are easily accumulated. Integrative clustering of such multi-omics data is crucial to recognize their latent structure and to acknowledge the correlation within and across them. Although the integrative analysis of diversified multi-omics data is informative, it is challenging when multiplicity in data inflicts poor accordance w.r.t. clustering structure. The objective of this work is to develop an effective integrative analysis framework that encapsulates the heterogeneity of various biological mechanisms and predicts homogeneous subgroups of cancer patients.

METHOD

In this paper, improved sparse-joint non-negative matrix factorization (sparse-jNMF) has been devised for the problem of cancer-subtype discovery. The initial points of sparse-jNMF have improved using a novel meta-heuristic algorithm adaptive gorilla troops optimizer (Ada-GTO). Improving the initialization of sparse-jNMF enhances its convergence behavior and further strengthens the clustering performance. In addition, the consensus clustering approach has been adopted to construct a patient-patient similarity matrix for obtaining stable clusters of patient samples.

RESULT

The proposed framework has been applied to 4 different real-life multi-omics cancer datasets, namely colon adenocarcinoma, breast-invasive carcinoma, kidney-renal clear-cell carcinoma, and lung adenocarcinoma. The proposed method results in patient clusters with better silhouette scores and cluster purity than classical initialization and similar meta-heuristics for initial point estimation approaches. Survival probabilities estimated using Kaplan-Meier (KM) curve show statistically significant difference (p < 0.05) for the homogenous cancer patient clusters obtained using the proposed method as compared to iCluster. The presented approach further identified the somatic mutations for the classified subgroups, which is beneficial to provide targeted treatments.

CONCLUSION

This paper proposes Ada-GTO guided sparse-jNMF framework for cancer subtype discovery, considering the information from multiple omic features that provide comprehension. The proposed meta-guided framework outperforms all other state-of-the-art counterparts. It also has great potential for obtaining the homogeneous subgroups of other diseases.

Enhanced chimp optimization algorithm for high level synthesis of digital filters

The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm

Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications.

CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems

Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho.

Hybrid leader based optimization: a new stochastic optimization algorithm for solving optimization applications

In this paper, a new optimization algorithm called hybrid leader-based optimization (HLBO) is introduced that is applicable in optimization challenges. The main idea of HLBO is to guide the algorithm population under the guidance of a hybrid leader. The stages of HLBO are modeled mathematically in two phases of exploration and exploitation. The efficiency of HLBO in optimization is tested by finding solutions to twenty-three standard benchmark functions of different types of unimodal and multimodal. The optimization results of unimodal functions indicate the high exploitation ability of HLBO in local search for better convergence to global optimal, while the optimization results of multimodal functions show the high exploration ability of HLBO in global search to accurately scan different areas of search space. In addition, the performance of HLBO on solving IEEE CEC 2017 benchmark functions including thirty objective functions is evaluated. The optimization results show the efficiency of HLBO in handling complex objective functions. The quality of the results obtained from HLBO is compared with the results of ten well-known algorithms. The simulation results show the superiority of HLBO in convergence to the global solution as well as the passage of optimally localized areas of the search space compared to ten competing algorithms. In addition, the implementation of HLBO on four engineering design issues demonstrates the applicability of HLBO in real-world problem solving.

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot’s trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.