scholarly journals ON SEARCH CAPABILITIES OF THE DIFFERENTIAL EVOLUTION ALGORITHM

2001 ◽  
Vol 1 ◽  
pp. 288
Author(s):  
Anatoly Sukov
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Search Technique ◽  
Technical Character ◽  
Evolution Algorithm

This paper examines the algorithm of differential evolution that has appeared rather recently. This algorithm ascribed by its developers to a class of evolutionary algorithms is a comparatively non-complicated technique o f solution search as applied to multiparameter optimisation tasks. Nevertheless, there are two essential factors preventing from wide application of the considered solution search technique. One of them lies in the principle of coding vectors (variables) that constitute a population the algorithm works with. The second problem is of pure technical character: in the process of search, stagnation occurs, or impossibility to find new solutions, when there is no optimal solution in the population and the vectors available are not heterogeneous. Besides studying search possibilities (limitations) of the differential evolution, some ways to cope with the problem of stagnation so-as to raise the performance of the algorithm are also suggested.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

Research on the Optimal Design of Soccer Robot Based on the Mechanical Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 3854-3857
Author(s):  
Jian Wen Han ◽  
Lei Hong
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Mechanical Analysis ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed

scholarly journals A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

2022 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Karn Moonsri ◽  
Kanchana Sethanan ◽  
Kongkidakhon Worasan
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Computational Time ◽  
Poultry Industry ◽  
Routing Problem ◽  
De Algorithm ◽  
Evolution Algorithm

Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

Stratified Sampling Differential Evolution Algorithm for Global Optimization Problem

2012 ◽  
Vol 452-453 ◽  
pp. 1491-1495
Author(s):  
Shu Hua Wen ◽  
Qing Bo Lu ◽  
Xue Liang Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Sampling Method ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Stratified Sampling ◽  
Local Optima ◽  
De Algorithm ◽  
Local Optimal Solution ◽  
Evolution Algorithm

Differential Evolution (DE) is one kind of evolution algorithm, which based on difference of individuals. DE has exhibited good performance on optimization problem. However, when a local optimal solution is reached with classical Differential Evolution, all individuals in the population gather around it, and escaping from these local optima becomes difficult. To avoid premature convergence of DE, we present in this paper a novel variant of DE algorithm, called SSDE, which uses the stratified sampling method to replace the random sampling method. The proposed SSDE algorithm is compared with some variant DE. The numerical results show that our approach is robust, competitive and fast.

scholarly journals Solution of Linear and Non-Linear Boundary Value Problems Using Population-Distributed Parallel Differential Evolution

2019 ◽  
Vol 9 (3) ◽  
pp. 205-218 ◽  
Author(s):  
Amnah Nasim ◽  
Laura Burattini ◽  
Muhammad Faisal Fateh ◽  
Aneela Zameer
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Boundary Value Problems ◽  
Boundary Value ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Entire Solution ◽  
Local Optima ◽  
And Behavior

Abstract Cases where the derivative of a boundary value problem does not exist or is constantly changing, traditional derivative can easily get stuck in the local optima or does not factually represent a constantly changing solution. Hence the need for evolutionary algorithms becomes evident. However, evolutionary algorithms are compute-intensive since they scan the entire solution space for an optimal solution. Larger populations and smaller step sizes allow for improved quality solution but results in an increase in the complexity of the optimization process. In this research a population-distributed implementation for differential evolution algorithm is presented for solving systems of 2nd-order, 2-point boundary value problems (BVPs). In this technique, the system is formulated as an optimization problem by the direct minimization of the overall individual residual error subject to the given constraint boundary conditions and is then solved using differential evolution in the sense that each of the derivatives is replaced by an appropriate difference quotient approximation. Four benchmark BVPs are solved using the proposed parallel framework for differential evolution to observe the speedup in the execution time. Meanwhile, the statistical analysis is provided to discover the effect of parametric changes such as an increase in population individuals and nodes representing features on the quality and behavior of the solutions found by differential evolution. The numerical results demonstrate that the algorithm is quite accurate and efficient for solving 2nd-order, 2-point BVPs.

Flight control clearance of the Cessna Citation X using evolutionary algorithms

2016 ◽  
Vol 231 (3) ◽  
pp. 510-532
Author(s):  
Yamina Boughari ◽  
Ruxandra Mihaela Botez ◽  
Georges Ghazi ◽  
Florian Theel
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Objective Function ◽  
Linear Model ◽  
Differential Evolution Algorithm ◽  
Good Level ◽  
Flying Qualities ◽  
Flight Envelope ◽  
Evolution Algorithm

In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft’s mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H∞ method and two different metaheuristic algorithms; in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H∞ weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft’s flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was concluded that very good performance of the business Cessna Citation X aircraft was achieved in this research.

scholarly journals Hybrid Taguchi-Differential Evolution Algorithm for Parameter Estimation of Differential Equation Models with Application to HIV Dynamics

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Wen-Hsien Ho ◽  
Agnes Lai-Fong Chan
Keyword(s):  
Parameter Estimation ◽  
Numerical Methods ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Dynamical Model ◽  
Hiv Dynamics ◽  
Differential Equation Models ◽  
Immunodeficiency Virus ◽  
Evolution Algorithm

This work emphasizes solving the problem of parameter estimation for a human immunodeficiency virus (HIV) dynamical model by using an improved differential evolution, which is called the hybrid Taguchi-differential evolution (HTDE). The HTDE, used to estimate parameters of an HIV dynamical model, can provide robust optimal solutions. In this work, the HTDE approach is effectively applied to solve the problem of parameter estimation for an HIV dynamical model and is also compared with the traditional differential evolution (DE) approach and the numerical methods presented in the literature. An illustrative example shows that the proposed HTDE gives an effective and robust way for obtaining optimal solution, and can get better results than the traditional DE approach and the numerical methods presented in the literature for an HIV dynamical model.

A Satisfaction Differential Evolution Algorithm for Detailed Machine Layout in Assembly Plant

2014 ◽  
Vol 926-930 ◽  
pp. 3346-3349
Author(s):  
Wei Zeng ◽  
Cheng Long Liu ◽  
Xiao Jun Zheng
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimization Theory ◽  
Optimal Solution ◽  
Assembly Plant ◽  
Machine Layout ◽  
Practical Engineering ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms

We herein present a satisfaction differential evolution algorithm to deal with machine layout in assembly plant. This paper firstly provide the assumption for detailed machine layout problem, build a mathematical model of detailed machine layout, and replace the optimal solution with satisfactory solution, combine satisfactory optimization theory with differential evolution algorithms, employ satisfaction function as the objective function of differential evolution algorithm. The experimental results verified our methods with practical engineering examples.

The Multi-Objective Differential Evolutionary Algorithms and its Application in Optimal Allocation of Water Resources

2012 ◽  
Vol 518-523 ◽  
pp. 4093-4096
Author(s):  
Yan Hong Long ◽  
Li Yang Yu
Keyword(s):  
Water Resources ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Swarm Intelligence ◽  
Optimal Allocation ◽  
Differential Evolution Algorithm ◽  
Multi Objective ◽  
Evolution Algorithm ◽  
Optimization Search

Abstract: Differential evolution algorithm (differential evolution DE) is a multi-objective evolutionary algorithm based on groups， which instructs optimization search by swarm intelligence produced by co-operation and competition among individuals within groups. This paper presents it to the research of optimal allocation of water resources. Accord to the application of the example, the results shows that reasonable and effective.

Sign in / Sign up

Export Citation Format

Share Document