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Agrawal, P., H. F. Abutarboush, T. Ganesh, and A. W. Mohamed, "Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)", IEEE Access, vol. 9, pp. 26766 - 26791, 2021. Abstract
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Agrawal, P., T. Ganesh, and A. W. Mohamed, A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection, , vol. 33, issue 11, pp. 5989 - 6008, 2021. AbstractWebsite

To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related metaheuristic algorithms were developed and solved this type of problem. It motivated us to check the performance of recently developed gaining–sharing knowledge-based optimization algorithm (GSK), which is based on the concept of gaining and sharing knowledge of humans throughout their lifespan. It depends on two stages: beginners–intermediate gaining and sharing stage and intermediate–experts gaining and sharing stage. In this study, two approaches are proposed to solve feature selection problems: FS-BGSK: a novel binary version of GSK algorithm that relies on these two stages with knowledge factor 1 and FS-pBGSK: a population reduction technique that is employed on BGSK algorithm to enhance the exploration and exploitation quality of FS-BGSK. The proposed approaches are checked on twenty two feature selection benchmark datasets from UCI repository that contains small, medium and large dimensions datasets. The obtained results are compared with seven state-of-the-art metaheuristic algorithms; binary differential evolution, binary particle swarm optimization algorithm, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm and binary salp swarm algorithm. It concludes that FS-pBGSK and FS-BGSK outperform other algorithms in terms of accuracy, convergence and robustness in most of the datasets.

Agrawal, P., T. Ganesh, D. Oliva, and A. W. Mohamed, S-shaped and V-shaped gaining-sharing knowledge-based algorithm for feature selection, , 2021. AbstractWebsite

In machine learning, searching for the optimal feature subset from the original datasets is a very challenging and prominent task. The metaheuristic algorithms are used in finding out the relevant, important features, that enhance the classification accuracy and save the resource time. Most of the algorithms have shown excellent performance in solving feature selection problems. A recently developed metaheuristic algorithm, gaining-sharing knowledge-based optimization algorithm (GSK), is considered for finding out the optimal feature subset. GSK algorithm was proposed over continuous search space; therefore, a total of eight S-shaped and V-shaped transfer functions are employed to solve the problems into binary search space. Additionally, a population reduction scheme is also employed with the transfer functions to enhance the performance of proposed approaches. It explores the search space efficiently and deletes the worst solutions from the search space, due to the updation of population size in every iteration. The proposed approaches are tested over twenty-one benchmark datasets from UCI repository. The obtained results are compared with state-of-the-art metaheuristic algorithms including binary differential evolution algorithm, binary particle swarm optimization, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm, binary salp swarm algorithm. Among eight transfer functions, V4 transfer function with population reduction on binary GSK algorithm outperforms other optimizers in terms of accuracy, fitness values and the minimal number of features. To investigate the results statistically, two non-parametric statistical tests are conducted that concludes the superiority of the proposed approach.

Agrawal, P., T. Ganesh, and A. W. Mohamed, "Solution of Uncertain Solid Transportation Problem by Integer Gaining Sharing Knowledge Based Optimization Algorithm", 2020 International Conference on Computational Performance Evaluation (ComPE), pp. 158 - 162, 2-4 July 2020, Submitted. Abstract
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Agrawal, P., T. Ganesh, and A. W. Mohamed, Chaotic gaining sharing knowledge-based optimization algorithm: an improved metaheuristic algorithm for feature selection, , vol. 25, issue 14, pp. 9505 - 9528, 2021. AbstractWebsite

The gaining sharing knowledge based optimization algorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates a modified version of the GSK algorithm to find the best feature subsets. Firstly, it represents a binary variant of GSK algorithm by employing a probability estimation operator (Bi-GSK) on the two main pillars of GSK algorithm. And then, the chaotic maps are used to enhance the performance of the proposed algorithm. Ten different types of chaotic maps are considered to adapt the parameters of the GSK algorithm that make a proper balance between exploration and exploitation and save the algorithm from premature convergence. To check the performance of proposed approaches of GSK algorithm, twenty-one benchmark datasets are taken from the UCI repository for feature selection. The performance is measured by calculating different type of measures, and several metaheuristic algorithms are adopted to compare the obtained results. The results indicate that Chebyshev chaotic map shows the best result among all chaotic maps which improve the performance accuracy and convergence rate of the original algorithm. Moreover, it outperforms the other metaheuristic algorithms in terms of efficiency, fitness value and the minimum number of selected features.

Agrawal, P., T. Ganesh, and A. W. Mohamed, Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm, , 2021. AbstractWebsite

This article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

Ali Wagdy Mohamed, Hegazy Zaher, M. K., "A Particle Swarm Approach for Solving Stochastic Optimization Problems", applied mathematics & information sciences, vol. 5, no. 3: naturalspublishing, pp. 379S–401S, 2011. Abstract
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Chen, E., J. Chen, A. W. Mohamed, B. Wang, Z. Wang, and Y. Chen, "Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization", IEEE Access, vol. 8, pp. 175660 - 175668, 2020. Abstract
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Cheng, L., Y. Wang, C. Wang, A. W. Mohamed, and T. Xiao, "Adaptive Differential Evolution Based on Successful Experience Information", IEEE Access, vol. 8, pp. 164611 - 164636, 2020. Abstract
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El-Qulity, S. A., and A. W. Mohamed, "A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm", Computational Intelligence and Neuroscience, vol. 2016: Hindawi Publishing Corporation, 2015. Abstract
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Hadi, A. A., A. W. Mohamed, and K. M. Jambi, "Single-Objective Real-Parameter Optimization: Enhanced LSHADE-SPACMA Algorithm", Heuristics for Optimization and Learning, Cham, Springer International Publishing, pp. 103 - 121, 2021. Abstract

Hadi, Anas A.Mohamed, Ali W.Jambi, Kamal M.Real parameter optimization is one of the active research fields during the last decade. The performance of LSHADE-SPACMALSHADE was competitive in IEEE CEC’2017 competition on Single Objective Bound Constrained Real-Parameter Single Objective Optimization. Besides, it was ranked fourth among twelve papers were presented on and compared to this new benchmark problems. In this work, an improved version named ELSHADE-SPACMASPACMA is introduced. In LSHADE-SPACMA, p value that controls the greediness of the mutation strategy is constant. While in ELSHADE-SPACMAESHADE, p value is dynamic. Larger value of p will enhance the exploration, while smaller values will enhance the exploitation. We further enhanced the performance of ELSHADE-SPACMA by integrating another directed mutation strategy within the hybridization framework. The proposed algorithm has been evaluated using IEEE CEC’2017 benchmark. According to the comparison results, the proposed ELSHADE-SPACMA algorithm is better than LSHADE and LSHADE-SPACMA. Besides, The comparison results between ELSHADE-SPACMA and the best three algorithms from the IEEE CEC’2017 Competition indicate that ELSHADE-SPACMA algorithm shows overall better performance and it is highly competitive algorithm for solving global optimization problems.

Hadi, A. A., A. W. Mohamed, and K. M. Jambi, LSHADE-SPA memetic framework for solving large-scale optimization problems, , vol. 5, issue 1, pp. 25 - 40, 2019. AbstractWebsite

During the last decade, large-scale global optimization has been one of the active research fields. Optimization algorithms are affected by the curse of dimensionality associated with this kind of complex problems. To solve this problem, a new memetic framework for solving large-scale global optimization problems is proposed in this paper. In the proposed framework, success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration, while a modified version of multiple trajectory search is used for local exploitation. The framework introduced in this paper is further enhanced by the concept of divide and conquer, where the dimensions are randomly divided into groups, and each group is solved separately. The proposed framework is evaluated using IEEE CEC2010 and the IEEE CEC2013 benchmarks designed for large-scale global optimization. The comparison results between our framework and other state-of-the-art algorithms indicate that our proposed framework is competitive in solving large-scale global optimization problems.

Hassan, S. A., P. Agrawal, T. Ganesh, and A. W. Mohamed, "Scheduling shuttle ambulance vehicles for COVID-19 quarantine cases, a multi-objective multiple 0–1 knapsack model with a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm", Data Science for COVID-19, pp. 675 - 698, 2021. AbstractWebsite

The purpose of this paper is to present a proposal for scheduling shuttle ambulance vehicles assigned to COVID-19 patients using one of the discrete optimization techniques, namely, the multi-objective multiple 0–1 knapsack problem. The scheduling aims at achieving the best utilization of the predetermined planning time slot; the best utilization is evaluated by maximizing the number of evacuated people who might be infected with the virus to the isolation hospital and maximizing the effectiveness of prioritizing the patients relative to their health status. The complete mathematical model for the problem is formulated including the representation of the decision variables, the problem constraints, and the multi-objective functions. The proposed multi-objective multiple knapsack model is applied to an illustrated case study in Cairo, Egypt, the case study aims at improving the scheduling of ambulance vehicles in the back and forth shuttle movements between patient’ locations and the isolation hospital. The case study is solved using a novel Discrete Binary Gaining-Sharing knowledge-based optimization algorithm (DBGSK). The detail procedure of the novel DBGSK is presented along with the complete steps for solving the case study.

Hassan, S. A., P. Agrawal, T. Ganesh, and A. W. Mohamed, "A Travelling Disinfection-Man Problem (TDP) for COVID-19: A Nonlinear Binary Constrained Gaining-Sharing Knowledge-Based Optimization Algorithm", Intelligent Data Analysis for COVID-19 Pandemic, Singapore, Springer Singapore, pp. 291 - 318, 2021. Abstract

An improved scheduling the disinfection process of the new coronavirus (COVID-19) is introduced. The scheduling aims at achieving the best utilization of the available day time, which is calculated as the total disinfection time minus the total loss travelling time. In this regard, a new application problem is presented, which is called a travelling disinfection-man problem (TDP). The new problem (TDP) in network optimization resemble somehow the famous travelling salesman problems (TSP) but with basic distinct variations where a disinfection group is likely to select a route to reach a subset of predetermined places to be disinfected with the most utilization of the available day working hours. A nonlinear binary model is introduced with a detailed real application case study involving the improving the scheduling of coronavirus disinfection process for five contaminated faculties in Ain Shams University in Cairo, and the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK).

Hassan, S. A., P. Agrawal, T. Ganesh, and A. W. Mohamed, "Optimum Distribution of Protective Materials for COVID−19 with a Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm", Computational Intelligence Techniques for Combating COVID-19, Cham, Springer International Publishing, pp. 135 - 157, 2021. Abstract

Many application problems are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. One of these practical applications is to optimally distribute protective materials for the newly emerged COVID-19. It is defined for a decision-maker who wants to choose a subset of candidate hospitals comprising the maximization of the distributed quantities of protective materials to a set of chosen hospitals within a specific time shift. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study; the case study is solved using a novel discrete binary gaining-sharing knowledge-based optimization algorithm (DBGSK). The solution algorithm proposes a novel binary adaptation of a recently developed gaining-sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge through their life span. Discrete binary version of GSK named novel binary gaining-sharing knowledge-based optimization algorithm (DBGSK) depends mainly on two binary stages: binary junior gaining-sharing stage and binary senior gaining-sharing stage with knowledge factor 1. These two stages enable DBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space.

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Mohamed, A. K., A. W. Mohamed, E. Z. Elfeky, and M. Saleh, "Solving Constrained Non-linear Integer and Mixed-Integer Global Optimization Problems Using Enhanced Directed Differential Evolution Algorithm", Machine Learning Paradigms: Theory and Application, Cham, Springer International Publishing, pp. 327 - 349, 2019. Abstract

This paper proposes an enhanced modified Differential Evolution algorithm (MI-EDDE) to solve global constrained optimization problems that consist of mixed/non-linear integer variables. The MI-EDDE algorithm, which is based on the constraints violation, introduces a new mutation rule that sort all individuals ascendingly due to their constraint violations (cv) value and then the population is divided into three clusters (best, better and worst) with sizes 100p%, (NP-2) * 100p% and 100p% respectively. Where p is the proportion of the partition with respect to the total number of individuals in the population (NP). MI-EDDE selects three random individuals, one of each partition to implement the mutation process. This new mutation scheme shown to enhance the global and local search capabilities and increases the convergence speed. Eighteen test problems with different features are tested to evaluate the performance of MI-EDDE, and a comparison is made with four state-of-the-art evolutionary algorithms. The results show superiority of MI-EDDE to the four algorithms in terms of the quality, efficiency and robustness of the final solutions. Moreover, MI-EDDE shows a superior performance in solving two high dimensional problems and finding better solutions than the known optimal solution.

Mohamed, A. W., "An Improved Differential Evolution Algorithm with Triangular Mutation for Global Numerical Optimization", Computers & Industrial Engineering, vol. 85: Elsevier, pp. 359–375, 2015. Abstract
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Mohamed, A. W., H. F. Abutarboush, A. A. Hadi, and A. K. Mohamed, "Gaining-Sharing Knowledge Based Algorithm With Adaptive Parameters for Engineering Optimization", IEEE Access, vol. 9, pp. 65934 - 65946, 2021. Abstract
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Mohamed, A. W., Solving stochastic programming problems using new approach to Differential Evolution algorithm, , vol. 18, issue 2, pp. 75 - 86, 2017. AbstractWebsite

This paper presents a new approach to Differential Evolution algorithm for solving stochastic programming problems, named DESP. The proposed algorithm introduces a new triangular mutation rule based on the convex combination vector of the triangle and the difference vector between the best and the worst individuals among the three randomly selected vectors. The proposed novel approach to mutation operator is shown to enhance the global and local search capabilities and to increase the convergence speed of the new algorithm compared with conventional DE. DESP uses Deb’s constraint handling technique based on feasibility and the sum of constraint violations without any additional parameters. Besides, a new dynamic tolerance technique to handle equality constraints is also adopted. Two models of stochastic programming (SP) problems are considered: Linear Stochastic Fractional Programming Problems and Multi-objective Stochastic Linear Programming Problems. The comparison results between the DESP and basic DE, basic particle swarm optimization (PSO), Genetic Algorithm (GA) and the available results from where it is indicated that the proposed DESP algorithm is competitive with, and in some cases superior to, other algorithms in terms of final solution quality, efficiency and robustness of the considered problems in comparison with the quoted results in the literature.

Mohamed, A. W., and H. Z. Sabry, "Constrained optimization based on modified differential evolution algorithm", Information Sciences, vol. 194: Elsevier, pp. 171–208, 2012. Abstract
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Mohamed, A. W., A. A. Hadi, and A. K. Mohamed, Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm, , vol. 11, issue 7, pp. 1501 - 1529, 2020. AbstractWebsite

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions.

Mohamed, A. W., and P. N. Suganthan, Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation, , vol. 22, issue 10, pp. 3215 - 3235, 2018. AbstractWebsite

This paper presents enhanced fitness-adaptive differential evolution algorithm with novel mutation (EFADE) for solving global numerical optimization problems over continuous space. A new triangular mutation operator is introduced. It is based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better and the worst individuals among the three randomly selected vectors. Triangular mutation operator helps the search for better balance between the global exploration ability and the local exploitation tendency as well as enhancing the convergence rate of the algorithm through the optimization process. Besides, two novel, effective adaptation schemes are used to update the control parameters to appropriate values without either extra parameters or prior knowledge of the characteristics of the optimization problem. In order to verify and analyze the performance of EFADE, numerical experiments on a set of 28 test problems from the CEC2013 benchmark for 10, 30 and 50 dimensions, including a comparison with 12 recent DE-based algorithms and six recent evolutionary algorithms, are executed. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, EFADE is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance.

Mohamed, A. W., "A Novel Discrete Binary Differential Evolution Algorithm", Aloy Journal of Soft Computing and Applications, vol. 2, no. 1: Aloy Publisher, 2014. Abstract
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