Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.

Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected, the Pareto ranking operator is used to enable a multiswarm subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman’s analysis while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD.

Deep data analysis for latent information prediction has been an important research area. Many of the existing solutions have used the textual data and have obtained an accurate results for predicting users’ interests and other latent attributes. However, little attention has been paid to visual data that is becoming increasingly popular in recent times. In this paper, we addresses the problem of discovering the attributed interest and of analyzing the performance of the automatic prediction using a comparison with the self assessed topics of interest (topics of interest provided by the user in a proposed questionnaire) based on data analysis techniques applied on the users visual data. We analyze the content of each user’s images to aggregate the image-level users’ interests distribution in order to obtain the user-level users’ interest distribution. To do this, we employ the pretrained ImageNet convolutional neural networks architectures for the feature extraction step and to construct the ontology, as the users’ interests model, in order to learn the semantic representation for the popular topics of interests defined by social networks (e.g., Facebook). Our experimental studies show that this analysis, on the most relevant features, enhances the performance of the prediction framework. In order to improve our framework’s robustness and generalization with unknown users’ profiles, we propose a novel database evaluation. Our proposed framework provided promising results which are competitive to state-of-the-art techniques with an accuracy of 0.80.

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection. Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed Bi-behaviorsCrow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized.

An innovative e-learning project is presented in this paper, which is a mobile workbook that teaches handwriting at school. This mobile application proposes a new qualitative and quantitative analysis process of online cursive handwriting. It gives a real-time feedback, detects mistakes and helps teachers evaluate children's writing skills. The main aim of this notebook is to aid kids learn how to write correctly. We analyze handwriting according to major criteria like shape, kinematics of the trace, position respect to the reference lines, stroke order and direction.

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a Convolutional Neural Network (CNN) and a Bidirectional Gated Recurrent Unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.

This paper defines a new Moth-Flame optimization version with Quantum behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, Newthyroid, Wine, LiverDisorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Fourty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, Newthyroid, Wine, LiverDisorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Fourty; and was able to provide the exact number of clusters for all datasets.