Browsing by Author "Mohamed, Ali Wagdy"

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Evaluating the Performance of Adaptive Gaining- Sharing Knowledge Based Algorithm on CEC 2020
(IEEE, 2020-09-07) Mohamed, Ali Wagdy; Hadi, Anas A; Mohamed, Ali Khater; Awad, Noor H
This paper introduces an enhancement of the recent developed Gaining Sharing Knowledge based algorithm, dubbed as GSK. This algorithm is an excellent example of a contemporary nature-based algorithm which is inspired from the human life behavior of gaining and sharing knowledge to solve the optimization task. GSK algorithm simulates the natural phenomena of human gaining and sharing knowledge using two main phases: junior and senior. A set of initial solutions are generated at the beginning of the search which are considered juniors. Later, the individuals are moving to senior stage by interacting with the environment and cooperating with other solutions during the search. The key idea in this work is to extend and improve the original GSK algorithm by proposing adaptive settings to the two important control parameters: knowledge factor and knowledge ratio. These two parameters are responsible to control junior and senior gaining and sharing phases between the solutions during the optimization loop. The algorithm is named AGSK and tested on the recent benchmark suite on bound constrained numerical optimization which consists of different challenging optimization problems with different dimensions. This benchmark is presented in IEEE-CEC2020 competition. When compared with other state-of-the-art algorithms including original GSK, AGSK shows superior performance.
Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems
(Springer London, 2022-09) Mohamed, Ali Wagdy; Sallam, Karam M; Agrawal, Prachi; Hadi, Anas A; Mohamed, Ali Khater
To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift and rotation. The different combinations of the binary operators are applied to the objective functions which leads to the CEC2021 benchmark functions. Therefore, different meta-heuristic algorithms are considered which solve the benchmark functions with different dimensions. The performance of some basic, advanced meta-heuristics algorithms and the algo- rithms that participated in the CEC2021 competition have been experimentally investigated and many observations, recommendations, conclusions have been reached. The experimental results show the performance of meta-heuristic algorithms on the different combinations of binary parameterized operators.
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorith
(Springer, 2019-12) Mohamed, Ali Wagdy; Hadi, Anas A.; Mohamed, Ali Khater
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