A Multi-Populated Differential Evolution Algorithm for Solving Constrained Optimization Problem

2006 ◽  
Author(s):  
M.F. Tasgetiren ◽  
P.N. Suganthan
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Constrained Optimization Problem ◽  
Evolution Algorithm

scholarly journals Application of Differential Evolution in the Solution of Stiff System of Ordinary Differential Equations

2020 ◽  
Vol 8 (1) ◽  
pp. 01-08
Author(s):  
Ashiribo Senapon Wusu ◽  
Olusola Olabanjo ◽  
Benjamin Aribisala
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Approximate Solutions ◽  
Constrained Optimization Problem ◽  
Stiff System ◽  
Order Ordinary Differential Equation ◽  
First Order ◽  
Evolutionary Optimization Algorithms ◽  
Evolution Algorithm

In recent times, the adaptation of evolutionary optimization algorithms for obtaining optimal solutions of many classical problems is gaining popularity. In this paper, optimal approximate solutions of initial--valued stiff system of first--order Ordinary Differential Equation (ODE) are obtained by converting the ODE into constrained optimization problem. The later is then solve via differential evolution algorithm. To illustrate the efficiency of the proposed approach, two numerical examples were considered. This approach showed significant improvement on the accuracy of the results produced compared with existing methods discussed in literature.

scholarly journals A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Dong ◽  
Yuping Wang
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Reference Point ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Fitness Function ◽  
Differential Evolution Algorithm ◽  
Feasible Region ◽  
Weighting Vector ◽  
Evolution Algorithm

The constrained optimization problem (COP) is converted into a biobjective optimization problem first, and then a new memetic differential evolution algorithm with dynamic preference is proposed for solving the converted problem. In the memetic algorithm, the global search, which uses differential evolution (DE) as the search scheme, is guided by a novel fitness function based on achievement scalarizing function (ASF). The novel fitness function constructed by a reference point and a weighting vector adjusts preference dynamically towards different objectives during evolution, in which the reference point and weighting vector are determined adapting to the current population. In the local search procedure, simplex crossover (SPX) is used as the search engine, which concentrates on the neighborhood embraced by both the best feasible and infeasible individuals and guides the search approaching the optimal solution from both sides of the boundary of the feasible region. As a result, the search can efficiently explore and exploit the search space. Numerical experiments on 22 well-known benchmark functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.

Reliability Analysis of Earth Slopes Using Differential Evolution

2010 ◽  
Vol 163-167 ◽  
pp. 3099-3102
Author(s):  
Fei Kang ◽  
Jun Jie Li ◽  
Zhen Yue Ma
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Reliability Analysis ◽  
Reliability Index ◽  
Optimization Problem ◽  
Slip Surface ◽  
Constrained Optimization Problem ◽  
Analysis Problem ◽  
Earth Slopes ◽  
Probabilistic Slope Stability Analysis

This paper presents a new method to simultaneously search for the minimum reliability index and the critical probabilistic slip surface of earth slopes. By introducing the Hasofer-Lind reliability index, the probabilistic slope stability analysis problem is modeled as a constrained optimization problem. A recently proposed intelligent global optimization algorithm differential evolution with a penalty function is adopted to solve the constrained optimization problem. Numerical results on two slopes show that the propose technique is efficient and practical to reliability analysis of earth slopes.

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