The popularity of social media and the rise of sarcasm in online conversations make sarcasm detection a crucial task in natural language processing (NLP), which still faces several challenges, including data imbalance and expression implicitness. This article presents a novel approach for sarcasm detection in Arabic short texts using deep learning techniques with a particular focus on addressing data imbalances. To ensure accurate and reliable analysis of Arabic short text data, care must be taken in considering and adapting existing techniques due to lack of standardization and changes in word forms. Thus, the proposed approach utilizes transformers to capture contextual knowledge and addresses the data imbalance problem by combining various data sampling and augmentation techniques. Using Arabic short text data, this article selects the most effective techniques for detecting sarcasm, which proposes a sampling technique that generates new samples from promising and representative words. The proposed approach was evaluated on several Arabic sarcasm detection datasets, including a new dataset we collected called ArSarcasticNews, a curated collection of Arabic short news texts for sarcasm detection. The experimental results across all datasets demonstrate promise. Specifically, the proposed model achieves F1 scores of 0.58, 0.60, 0.84, and 0.64 for the sarcastic class on ArSarcasm-v2, iSarcasmEval, IDAT data, and ArSarcasticNews, respectively. The study highlights the importance of understanding the context between sentences in identifying sarcasm in short Arabic texts and the potential of data augmentation techniques to improve model performance. Moreover, the use of the SHAP-based model provides valuable insight into the features used by the model to detect sarcasm and can aid in identifying potential biases in the model. Based on the findings, this study improves sarcasm detection and outperforms other approaches by analyzing dialectical short text and addressing data imbalances.

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems.

Social media is a common medium for expression of views, discussion, sharing of content, and promotion of products and ideas. These views are either polite or obscene. The growth of hate speech is one of the negative aspects of the medium and its emergence poses risk factors for society at various levels. Although there are rules and laws for these platforms, they cannot oversee and control all types of content. Thus, there is an urgent need to develop modern algorithms to automatically detect hateful content on social media. Arab society is not isolated from the world, and the usage of social media by its members has highlighted the importance of automated systems that help build an electronic society free of hate and aggression. This article aims to detect hate speech based on Arabic context over the Twitter platform by proposing different novel deep learning architectures in order to provide a thorough analytical study. Also, a comparative study is presented with a different well-known machine learning algorithm, as well as other state-of-the-art algorithms from the literature to be used as a beacon for interested researchers. These models have been applied to the Arabic tweets dataset, which included 15K tweets and 14 features. After training these models, the results obtained for the top two models included an improved bidirectional long short-term memory with an accuracy of 92.20% and a macro F1-score of 92% and a modified convolutional neural network with an accuracy of 92.10% and a macro F1-score of 91%. The results also showed the superiority of the performance of the deep learning models over other models in terms of accuracy.

Real-life problems can be expressed as optimization problems. These problems pose a challenge for researchers to design efficient algorithms that are capable of finding optimal solutions with the least budget. Stochastic Fractal Search (SFS) proved its powerfulness as a metaheuristic algorithm through the large research body that used it to optimize different industrial and engineering tasks. Nevertheless, as with any meta-heuristic algorithm and according to the “No Free Lunch” theorem, SFS may suffer from immature convergence and local minima trap. Thus, to address these issues, a popular Differential Evolution variant called Success-History based Adaptive Differential Evolution (SHADE) is used to enhance SFS performance in a unique three-phase hybrid framework. Moreover, a local search is also incorporated into the proposed framework to refine the quality of the generated solution and accelerate the hybrid algorithm convergence speed. The proposed hybrid algorithm, namely eMpSDE, is tested against a diverse set of varying complexity optimization problems, consisting of well-known standard unconstrained unimodal and multimodal test functions and some constrained engineering design problems. Then, a comparative analysis of the performance of the proposed hybrid algorithm is carried out with the recent state of art algorithms to validate its competitivity.

In smart grid, energy resource management is highly complex large-scale optimization problem where the aim is to maximize the incomes while minimizing operational costs. Due to presence of mixed-integer variables and non-linear constraints, recently the use of evolutionary algorithms as a tool to find optimal and near-optimal solutions is becoming popular. The energy resource management problem further gets complicated if the uncertainty associated with the renewable generation, load forecast errors, electric vehicles scheduling and market prices are considered. Therefore, in a real-world scenario, it is essential to address the issues brought by the variability of demand, renewable energy, electric vehicles, and market price variations while maximizing the incomes and minimizing the total operation costs. In this paper, we analyze the performance of Differential Evolution with a stochastic selection on a large-scale energy resource management problem with uncertainty designed for competition at CEC 2018. The system comprises of a 25-bus microgrid representing a residential area with high penetration of Distributed Energy Resources (DER), Electric Vehicles (EVs), Demand Response (DR) programs etc.

Many Differential Evolution algorithms are introduced in the literature to solve optimization problems with diverse set of characteristics. In this paper, we propose an extension of the previously published paper LSHADE-EpSin that was ranked as the joint winner in the real-parameter single objective optimization competition, CEC 2016. The contribution of this work constitutes two major modifications that have been added to enhance the performance: ensemble of sinusoidal approaches based on performance adaptation and covariance matrix learning for the crossover operator. Two sinusoidal waves have been used to adapt the scaling factor: non-adaptive sinusoidal decreasing adjustment and an adaptive sinusoidal increasing adjustment. Instead of choosing one of the sinusoidal waves randomly, a performance adaptation scheme based on earlier success is used in this work. Moreover, covariance matrix learning with Euclidean neighborhood is used for the crossover operator to establish a suitable coordinate system, and to enhance the capability of LSHADE-EpSin to tackle problems with high correlation between the variables. The proposed algorithm, namely LSHADE-cnEpSin, is tested on the IEEE CEC2017 problems used in the Special Session and Competitions on Single Objective Bound Constrained Real-Parameter Single Objective Optimization. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to other state-of-the-art algorithms.

Solving real-world optimization problems is considered a challenging task. This is due to the variability of the characteristics in objective functions, the presence of enormous number of local optima within the search space and highly nonlinear constraints with large number of variables. The advances on this type of problems are of capital importance for many researchers to develop new efficient evolutionary algorithms to tackle such problems in an efficient manner with better solutions. For this reason, this work proposes a new crossover technique based on covariance learning with Euclidean neighborhood which has been incorporated in the basic L-SHADE algorithm. The goal of this new technique is to help L-SHADE establish a suitable coordinate system for the crossover operator. This helps enhance L-SHADE capability to solve real world problems with difficult characteristics and nonlinear constraints. The proposed algorithm, namely L-covnSHADE, is tested on one of the challenging benchmarks which is the IEEE CEC'11 on real-world numerical optimization problems. This set consists of 22 real-world problems with diverse stimulating characteristics and a dimensionality ranging from 1 to 240 dimensions. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to the L-SHADE algorithm and other state-of-the-art algorithms including the winner of the CEC2011 competition.

Multilevel inverters are mainly used for DC to AC power conversion and these inverters can be classified into types current source inverter (CSI) and voltage source inverter (VSI). Voltage source inverters are more common in power industry to convert lower levels of DC voltages into higher levels of AC voltages. In the process of conversion widely implemented pulse width modulated (PWM) switching technique of DC sources introduces harmonics in inverter output voltage. Total harmonic distortion (THD) is a measure of harmonic pollution in the power system and it is observed that variations in both DC voltages and switching angles of inverter affect the THD of inverter output voltage. Cascaded multilevel symmetric inverters ideally have DC sources all equal and constant. This paper considers inverters where DC sources can be unequal, a justifiable and realistic supposition. Optimal values of DC voltages and switching angles, which minimize THD level, are found using evolutionary algorithm. An advanced form of Differential Evolution (DE), called LSHADE-EpSin, is applied for the optimization problem. SHADE is a success history based parameter adaptation technique of DE. LSHADE improves the performance of SHADE with linearly reducing the population size in successive generations. LSHADE-EpSin introduces an additional adaptation technique for control parameters of the evolutionary algorithm. The algorithm has successfully been implemented for higher levels of inverters considered in the scope of our research study.

Developing efficient evolutionary algorithms attracts many researchers due to the existence of optimization problems in numerous real-world applications. A new differential evolution algorithm, sTDE-dR, is proposed to improve the search quality, avoid premature convergence, and stagnation. The population is clustered in multiple tribes and utilizes an ensemble of different mutation and crossover strategies. In this algorithm, a competitive success-based scheme is introduced to determine the life cycle of each tribe and its participation ratio for the next generation. In each tribe, a different adaptive scheme is used to control the scaling factor and crossover rate. The mean success of each subgroup is used to calculate the ratio of its participation for the next generation. This guarantees that successful tribes with the best adaptive schemes are only the ones that guide the search toward the optimal solution. The population size is dynamically reduced using a dynamic reduction method. Comprehensive comparison of the proposed heuristic over a challenging set of benchmarks from the CEC2014 real parameter single objective competition against several state-of-the-art algorithms is performed. The results affirm robustness of the proposed approach compared to other state-of-the-art algorithms.

Evolutionary algorithms are successfully developed to handle the challenges in solving optimization problems with complex landscapes. Differential evolution proves its efficiency as a powerful evolutionary algorithm to solve complex optimization problems with diverse characteristics. In this paper, we aim at designing an enhanced evolutionary algorithm that embeds Differential Evolution in Stochastic Fractal Search. Stochastic Fractal Search is developed recently as a powerful metaheuristic algorithm that imitates the natural phenomenon of growth and uses the diffusion process based on random fractals. In this paper, we introduce a new adjustment to the Diffusion Process of Stochastic Fractal Search. The proposed algorithm namely, SFS-DPDE-GW, uses Differential Evolution in the Diffusion Process along with the Gaussian Walks to enhance the search. To validate the performance of our algorithm, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The proposed combination clearly enhances the performance of Stochastic Fractal Search and increases the efficiency of the update process which was incorporated after Diffusion process. Comparative studies show that the new algorithm has a superior performance compared to the original Stochastic Fractal Search and other recent state-of-the-art algorithms.

An effective and efficient self-adaptation framework is proposed to improve the performance of the L-SHADE algorithm by providing successful alternative adaptation for the selection of control parameters. The proposed algorithm, namely LSHADE-EpSin, uses a new ensemble sinusoidal approach to automatically adapt the values of the scaling factor of the Differential Evolution algorithm. This ensemble approach consists of a mixture of two sinusoidal formulas: A non-Adaptive Sinusoidal Decreasing Adjustment and an adaptive History-based Sinusoidal Increasing Adjustment. The objective of this sinusoidal ensemble approach is to find an effective balance between the exploitation of the already found best solutions, and the exploration of non-visited regions. A local search method based on Gaussian Walks is used at later generations to increase the exploitation ability of LSHADE-EpSin. The proposed algorithm is tested on the IEEE CEC2014 problems used in the Special Session and Competitions on Real-Parameter Single Objective Optimization of the IEEE CEC2016. The results statistically affirm the efficiency and robustness of the proposed approach to obtain better results compared to L-SHADE algorithm and other state-of-the-art algorithms.

Many researchers have developed population-based techniques to solve numerical optimization problems. Almost none of these techniques demonstrate consistent performance over a wide range of problems as these problems differ substantially in their characteristics. In the state-of-the-art cultural algorithms (CAs), problem solving is facilitated by the exchange of knowledge between a network of active knowledge sources in the belief space and networks of individuals in the population space. To enhance the performance of CAs, we restructure the social fabric interconnections to facilitate flexible communication among problem solvers in the population space. Several social network reconfiguration mechanisms and types of communications are examined. This extended CA is compared with other variants of CAs and other well-known state-of-the-art algorithms on a set of challenging real-world problems. The numerical results show that the injection of neighborhoods with flexible subnetworks enhances performance on a diverse landscape of numerical optimization problems.

This paper introduces a Cluster-based Differential Evolution Algorithm with Heterogeneous Influence for solving complex optimization problems. The idea behind this combination is to classify the Differential Evolution population into a number of clusters using k-means clustering method and to apply different mutation strategies for the clusters. The number of clusters is changed dynamically in each generation. The proposed algorithm uses three mutation strategies: DE/best-group/ 1/exp, DE/rand1/exp and DE/rand/1/bin. The DE/best-group/ 1/exp is an improved mutation strategy that randomly selects a portion of the population and then chooses the best individual in the group to guide the evolution. The k-means clustering algorithm is used periodically to fine-tune solutions that are generated from DE/best-group/1/exp by producing new clusters. This helps in balancing the exploration and exploitation capabilities by using different mutation strategies for these clusters to enhance diversity. The performance of the proposed approach is tested on 25 complex benchmark functions on single objective real-parameter numerical optimization. Results show that the proposed algorithm exhibits competitive performance when compared to other state-of-the-art algorithms.

Developing efficient evolutionary algorithms for solving learning-based real-parameter single objective optimization is a very challenging and essential task in many real applications. This task involves finding the best optimal solution with least computational cost, avoiding premature convergence. This paper proposes a new efficient Differential Evolution algorithm with success-based parameter adaptation with resizing population space. We introduce a new technique to adapt the control parameters which uses a memory-based structure of previous successful settings. Moreover, the population size is adapted linearly to find the most suitable size which helps to guide the search in each optimization loop. The proposed algorithm is tested on the benchmarks of the CEC2015 real parameter single objective competition. The results affirm the efficiency and robustness of our approach to reach good results.

Cultural Algorithms have been previously employed to model the emergence of cooperative behaviors of agents in different multi-agent systems. In this paper, a simplified and adaptive version will be used as the basis to generate cooperative behaviors within a team of soccer players using different team formations and effective plays. This system can be used as a tutorial for the application of Cultural Algorithms for the coordination of groups of agents in complex multi-agent dynamic environments. Simplified Cultural Algorithms were successful in effectively learning different types of plays, including active and passive protagonists, within a small number of generations. Successful learning includes the coordination of adjustments of the team members to develop the most suitable team formations for every scenario. Experimental results enable us to conclude that Cultural Algorithms, when configured properly, in order to produce significant results, can perform very competitively when compared to other types of learning strategies and case-based game plays.

Many meta-heuristics methods are applied to guide the exploration and exploitation of the search space for large scale optimization problems. These problems have attracted much attention from researchers who proposed developed a variety of techniques for locating the optimal solutions. Cultural Algorithm has been recently adopted to solve global numerical optimization problems. In this paper, a modified version of Cultural Algorithm (CA) that uses four knowledge sources in order to incorporate the information obtained from the objective function as well as constraint violation into knowledge structure in the belief space is proposed. The archived knowledge in the proposed approach will be used to enhance the way the belief space influences future generations of problem solvers. The first step is to use the four knowledge sources to guide the direction of the search to more promising solutions. The search is balanced between exploration and exploitation by dynamically adjusting the number of evaluations available for each type of knowledge source based on whether is primarily exploratory or exploitative. The second step selects one local search method to find the nearest solutions to those proposed by the knowledge sources. The proposed work is employed to solve seven global optimization problems in 50 and 100 dimensions, and an engineering application problem. Simulation results show how the approach speeds up the convergence process with very competitive results on such complex benchmarks when compared to other state-of-the-art algorithms.

Many evolutionary computational models have been introduced for solving engineering optimization problems that usually intend to find the global optimum solution. These methods, however, expose high computational effort and lack the diversity of the population and hence remain trapped in a local optimum. In this paper, we propose new hybrid optimization model, where a version of niche Cultural Algorithm is integrated with Tabu Search to guide the fittest individuals to new promising areas, aiming to escape local optima. The proposed approach significantly improves the performance of Cultural Algorithm by maintaining a high diversity among the population of problem solvers. This helps avoid premature and enhances located solutions. The technique is tested using a set of real-parameter optimization benchmark problems. The results in all cases indicate that the proposed method is capable of obtaining the optimal solutions with small number of function evaluations.

In this paper we propose an optimization algorithm for global optimization problems. The proposed algorithm is named (CA-ImLS) and is based on Cultural Algorithms and an improved local search approach for optimization over large-scale continuous spaces. In this paper, Cultural Algorithm and an improved sub-regional local search method are hybridized to form CA-ImLS. The original Cultural Algorithm is extended to have five parallel local searches that are rooted to its knowledge sources in the belief space component. This directs the search in multi-directions and improves the capability of its problem solvers in obtaining better-quality solutions. The distribution of new search agents is based on the success of the knowledge sources in which each knowledge source has its own local search for generating new agents with better fitness values and enhanced diversity to avoid stagnation. Experimental results are given for a set of benchmark optimization functions. Results indicate an average improvement of 2%-83% over the basic Cultural Algorithm framework.

Over the last three decades, many algorithms have been introduced for solving optimization problems of various complexity. Previous work in the optimization field on practical problems, using Cultural Algorithms, had shown that cultural learning emerged as the result of meta-level swarming of knowledge sources. This paper explores the use of meaningful neighbors in Cultural Algorithms for the constructed social metaphor. The algorithm uses an enhanced multi-layer tactical restructuring to dynamically change the topology of agents in the formed networks, periodically during an algorithm run as a diversity preserving-measure. The approach has been applied to solve the set of real world problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. Our results suggest that under appropriate parameter settings, the use of modified graphs of neighborhoods with a probabilistic disruptive re-structuring of the topology produces better results on the considered test functions compared to the best known scores of other algorithms from the literature.

In this paper we investigate the emergence and power of a complex social system based upon principles of cultural evolution. Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. Here we extend the influence and integration function in Cultural Algorithms by adding a mechanism by which knowledge sources can spread their influence throughout a population in the presence of heterogeneous layered social network. The interaction (overlapping) of the knowledge sources, represented as bounding boxes on the landscape, at the right level projects how efficient the cooperation is between the agents in the resultant "Social Network". The inter-related structures that emerge with this approach are critical to the effective functioning of the approach. We view these structures as constituting a "normal form" for Cultures within these real valued optimization landscapes. Our goal will be to identify the minimum social structure needed to solve problems of certain complexities. If this can be accomplished, it means that there will be a correspondence between the social structure and the problem environment in which it emerged. An escalating sequence of complex benchmark problems to our system will be presented. We conclude by suggesting the emergent features are what give cultural systems their power to learn and adapt.

Robustness is a key concern when developing a successful commercial evolutionary tool. In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic. In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities. Based on previous cultural algorithm approaches, we were to extend the existing models to produce a more general one that could be applied across all complexity classes. We then applied the system to the solution of a 150 randomly generated problems that ranged from simple to chaotic complexity classes. As a result we were able to produce the following conclusions: No homogeneous Social Fabric tested was dominant over all categories of complexity. As the complexity of problems increased, so did the complexity of the Social Fabric that was needed to deal with it efficiently. In other words, there was experimental evidence that social structure can be related to the frequency and complexity type of the problems that are presented to a cultural system.

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. While many successful real-world applications of Cultural Algorithms have been produced, we are interested in studying the fundamental computational processes involved the use of Cultural Systems as problem solvers. In previous work the influence of the knowledge sources have been on individuals in the population only. In this paper we introduce the notion of a social fabric in which the expression of knowledge sources can be distributed through the population. We apply the social fabric function to the solution of a tension/compression spring design problem. We show that different parameter combinations can affect the rate of solution.

Recently there has been increased interest in socially motivated approaches to problem solving. These approaches include particle swarm optimization, ant colony optimization, and cultural algorithms. Each of these approaches is derived from a social system that operates on potentially different scale. In previous work we introduced a toolkit to model optimization problem solving using cultural algorithms. In this paper we extend the influence and integration function in the cultural algorithm toolkit (CAT) by adding a mechanism by which knowledge sources can spread their influence throughout a population. We then compare this enhanced approach with previous approaches using the Cones world optimization landscape. Dejong and Morrison proposed the Cones world as an alternative to traditional benchmark optimization problems in the assessment of optimization algorithms. We demonstrate how the social fabric enhances cultural algorithm performance within this environment relative to earlier system.

Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems. While many successful real- world applications of cultural algorithms have been produced, we are interested in studying the fundamental computational processes involved in the use of cultural systems as problem solvers. Here we describe a Java-based toolkit system, the cultural algorithm toolkit (CAT) developed in the repast symphony simulation environment. The system allows users to easily configure and visualize the problem solving process of a cultural algorithm. Currently the system supports predator/prey problem solving in a "cones world" environment as well as a suite of benchmark problems in engineering design. Example runs of a predator prey example are presented to demonstrate the systems' capabilities.

Our conceptual model of hunter-gatherer search methods, vector voting, was developed based on a decision-making model. A multi-agent simulation model (currently adopted to be viewed as a game) was developed based on this model. Experiments are performed using this model and the results are analyzed to explore the impact of decision-making methods and resource sharing methods on population survival. The results suggest the fixed order strategies outperform equalitarian strategies head to head.