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The bird mating optimizer is a new metaheuristic algorithm that was originally proposed to solve continuous optimization problems with a very promising performance. However, the algorithm has not yet been applied for solving combinatorial optimization problems. Thus, the formulation may not be able to generate a discrete feasible solution. Many continuous algorithms used random-key representation to represent the discrete solution using real numbers or a discrete variant of the algorithm is used to deal with the discrete solution of the problem. However, there is no evidence which methodology is better for solving combinatorial optimization problems. Therefore, this work proposes two variants of bird mating optimizer (random-key bird mating optimizer and the discrete bird mating optimizer), to identify which one is more efficient in solving combinatorial optimization problems. In the first one, we use a random-key encoding scheme, whilst, in the later one, we use crossover (multi-parent) and mutation operators to combine the components of the selected parents to generate new broods. The performance of these algorithms is tested on the travelling salesman problem and berth allocation problem, and are compared with the results of two well-known optimization algorithms: Genetic Algorithm and Particle Swarm Optimization. Experimental results show that the discrete bird mating optimizer is more efficient than the others on all tested benchmark instances. Indeed, it is able to attain the best-known results in some of the BAP benchmark instances. These indicate the applicability and the effectiveness of the proposed discrete bird mating optimizer in solving combinatorial optimization problems. INDEX TERMS Heuristics, bird mating optimizer, berth allocation problem, travelling salesman problem, random-key, combinatorial optimization, metaheuristics.

Writer's identification from a handwritten text is one of the most challenging machines learning problems because of the variable handwritten sources, various languages, the similarity between writer's pattern, context variation, and implicit characteristics of handwriting styles. In this paper, a combination of the deep and hand-crafted descriptor is utilized to learn patterns from the handwritten images. First, to do so, the local patches are extracted from the handwritten images. Then, these patches are simultaneously fed to deep and hand-crafted descriptors to generate the local descriptions. The extracted local features are then assembled to make the whole description matrix. Finally, by applying the vector of locally aggregated descriptors (VLAD) encoding on the description matrix, a 1-D feature vector is extracted to represent the writer's pattern. It is worthwhile to mention that the generated description does not rely on any language model or context information. Thus, the proposed approach is language and content independent. In addition, the proposed method does not have any restriction on the input length, hence, the writer's sample can be a passage, paragraph, line, sentence, or even a word. The obtained results on three public benchmark datasets of IAM, CVL, and Khatt indicate that the proposed method has a high-accuracy rate in writing identification task. Furthermore, the performance of the proposed method on CVL dataset using both German and English samples demonstrates that the proposed approach has a high capability in learning a writer's pattern from both languages at the same time. INDEX TERMS Writer identification, deep descriptor, hand-crafted feature, feature fusion, feature length independent.

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector.

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