Ali Wagdy Mohamed

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.

Adaptive guided differential evolution algorithm (AGDE) is a differential evolution (DE) algorithm that utilizes the information of good and bad vectors in the population, it introduced a novel mutation rule in order to balance effectively the exploration and exploitation tradeoffs. It divided the population into three clusters (best, better and worst) with sizes 100p%, NP − 2 * 100% and 100% respectively. where p is the proportion of the partition with respect to the total number of individuals in the population (NP). AGDE selects three random individuals, one of each partition to implement the mutation process. Besides, a novel adaptation scheme was proposed in order to update the value of crossover rate without previous knowledge about the characteristics of the problems. This paper introduces enhanced AGDE (EAGDE) with non-linear population size reduction, which gradually decreases the population size according to a non-linear function. Moreover, a newly developed rule developed to determine the initial population size, that is related to the dimensionality of the problems. The performance of the proposed algorithm is evaluated using CEC2013 benchmarks and the results are compared with the state-of-art DE and non-DE algorithms, the results showed a great competitiveness for the proposed algorithm over the other algorithms, and the original AGDE.

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.

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.

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.

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).

Adaptive guided differential evolution algorithm (AGDE) is a DE algorithm that utilizes the information of good and bad vectors in the population, it introduced a novel mutation rule in order to balance effectively the exploration and exploitation tradeoffs. It divided the population 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). AGDE selects three random individuals, one of each partition to implement the mutation process. Besides, a novel adaptation scheme was proposed in order to update the value of crossover rate without previous knowledge about the characteristics of the problems. This paper introduces enhanced AGDE (EAGDE) with non-linear population size reduction, which gradually decreases the population size according to a non-linear function. Moreover, a newly developed rule developed to determine the initial population size, that is related to the dimensionality of the problems.