A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

El-Quliti, Said Ali Hassan; Ragab, Abdul Hamid M.; Abdelaal, Reda; Mohamed, Ali Wagdy; Mashat, Abdulfattah S.; Noaman, Amin Y.; Altalhi, Abdulrahman H.

doi:10.1155/2015/892937

Cited by 18 publications

(8 citation statements)

References 19 publications

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“…e obtained results show the robustness and efficiency of the modified differential evolution. Besides, they applied advanced versions of DE-based algorithms to solve many other real-world applications [30][31][32][33][34][35][36][37][38][39][40][41]. Gomes [42] solved the truss mass optimization problem using particle swarm procedure with nonlinear dynamic constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimum Scheduling the Electric Distribution Substations with a Case Study: An Integer Gaining-Sharing Knowledge-Based Metaheuristic Algorithm

et al. 2020

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This work is dedicated to the economic scheduling of the required electric stations in the upcoming 10-year long-term plan. The calculation of the required electric stations is carried out by estimating the yearly consumption of electricity over a long-time plan and then determining the required number of stations. The aim is to minimize the total establishing and operating costs of the stations based on a mathematical programming model with nonlinear objective function and integer decision variables. The introduced model is applied for a real practical case study to conclude the number of yearly constructed stations over a long-term plan in the electricity sector in Jeddah City, Saudi Arabia. The current planning method is based only on intuition by constructing the same number of required stations in each year without searching for better solutions. To solve the introduced mathematical model, a novel recent gaining sharing knowledge-based algorithm, named GSK, has been used. The Augmented Lagrangian Method (ALM) is applied to transform the constrained formulation to become unconstrained with penalization to the objective function. According to the obtained results of the real case study, the proposed GSK with ALM approved an ability to solve this case with respect to convergence, efficiency, quality, and robustness.

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Section: Literature Reviewmentioning

confidence: 99%

Optimum Scheduling the Electric Distribution Substations with a Case Study: An Integer Gaining-Sharing Knowledge-Based Metaheuristic Algorithm

et al. 2020

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“…(4) The triangular mutation rule is only used in this work, but in the previous work [47], the triangular mutation strategy is embedded into the DE algorithm and combined with the basic mutation strategy DE/rand/1/bin through a nonlinear decreasing probability rule. (5) In previous work [47] a restart mechanism based on Random Mutation and modified BGA mutation is used to avoid stagnation or premature convergence, whereas this work does not.…”

Section: Parameter Adaptation Schemes In Andementioning

confidence: 99%

“…The advantages are its simplicity of implementation, ease of use, speed, and robustness. Due to these advantages, it has been successfully applied for solving many real-world applications, like admission capacity planning in higher education [5,6], financial markets dynamic modeling [7], solar energy [8], and many others. In addition, many recent studies prove that the performance of DE is highly competitive with and in many cases superior to other EAs in solving unconstrained optimization problems, constrained optimization problems, multiobjective optimization problems, and other complex optimization problems [9].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

Mohamed

Almazyad

2017

Applied Computational Intelligence and Soft Computing

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This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule 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. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

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“…The DE is a population-based EA, which has been widely used to solve numerous optimization problems in different fields of science and engineering [15,16]. Easy implementation, compact structure, reliability, and robustness are the main advantages of the DE algorithm [15].…”

Section: Introductionmentioning

confidence: 99%

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020

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This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

Cited by 18 publications

References 19 publications

Optimum Scheduling the Electric Distribution Substations with a Case Study: An Integer Gaining-Sharing Knowledge-Based Metaheuristic Algorithm

Optimum Scheduling the Electric Distribution Substations with a Case Study: An Integer Gaining-Sharing Knowledge-Based Metaheuristic Algorithm

Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

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