Improved differential evolution algorithm based on chaotic theory and a novel Hill-Valley method for large-scale multimodal optimization problems

2015 ◽  
Author(s):  
Parisa Molavi Damanahi ◽  
Gelareh Veisi ◽  
Seyyed Javad Seyyed Mahdavi Chabok
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Multimodal Optimization ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Chaotic Theory

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

2014 ◽  
Vol 1046 ◽  
pp. 367-370
Author(s):  
Yu Zhou ◽  
Yong Bin Li ◽  
Zhong Zheng Shi ◽  
Zheng Xin Li ◽  
Lei Zhang
Keyword(s):  
Differential Evolution ◽  
Goal Programming ◽  
Large Scale ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Goal Programming Model ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

scholarly journals Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 8113
Author(s):  
Sherif S.M. Ghoneim ◽  
Mohamed F. Kotb ◽  
Hany M. Hasanien ◽  
Mosleh M. Alharthi ◽  
Attia A. El-Fergany
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Modal Analysis ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Stability Index ◽  
Pareto Solution ◽  
Evolution Algorithm

A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorporated. The results generated by the SpDEA are investigated and compared to standard multi-objective differential evolution (MODE) to prove their viability. The best answer is chosen carefully among trade-off Pareto points by using the technique of fuzzy Pareto solution. Two power system networks such as IEEE 30-bus and 118-bus systems as large-scale optimization problems with 129 design control variables are utilized to point out the effectiveness of the SpDEA. The realized results among many independent runs indicate the robustness of the SpDEA-based approach on OPF methodology in optimizing the defined objectives simultaneously.

scholarly journals Global optimization of laminated composite beams using an improved differential evolution algorithm

2020 ◽  
Vol 14 (1) ◽  
pp. 54-64
Author(s):  
Lam Thuan Phat ◽  
Nguyen Nhat Phi Long ◽  
Nguyen Hoai Son ◽  
Ho Huu Vinh ◽  
Le Anh Thang
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Design Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Laminated Composite ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential Evolution (DE) is an efficient and effective algorithm recently proposed for solving optimization problems. In this paper, an improved version of Differential Evolution algorithm, called iDE, is introduced to solve design optimization problems of composite laminated beams. The beams used in this research are Timoshenko beam models computed based on analytical formula. The iDE is formed by modifying the mutation and the selection step of the original algorithm. Particularly, individuals involved in mutation were chosen by Roulette wheel selection via acceptant stochastic instead of the random selection. Meanwhile, in selection phase, the elitist operator is used for the selection progress instead of basic selection in the optimization process of the original DE algorithm. The proposed method is then applied to solve two problems of lightweight design optimization of the Timoshenko laminated composite beam with discrete variables. Numerical results obtained have been compared with those of the references and proved the effectiveness and efficiency of the proposed method. Keywords: improved Differential Evolution algorithm; Timoshenko composite laminated beam; elitist operator; Roulette wheel selection; deterministic global optimization.

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