scholarly journals Differential Evolution Algorithm for Multilevel Assignment Problem: A Case Study in Chicken Transportation

2018 ◽  
Vol 23 (4) ◽  
pp. 55 ◽  
Author(s):  
Sasitorn Kaewman ◽  
Tassin Srivarapongse ◽  
Chalermchat Theeraviriya ◽  
Ganokgarn Jirasirilerd
Keyword(s):  
Differential Evolution ◽  
Assignment Problem ◽  
Differential Evolution Algorithm ◽  
Computation Time ◽  
Search Performance ◽  
De Algorithm ◽  
Minimum Number ◽  
Average Computation Time ◽  
Two Stages

This study aims to solve the real-world multistage assignment problem. The proposed problem is composed of two stages of assignment: (1) different types of trucks are assigned to chicken farms to transport young chickens to egg farms, and (2) chicken farms are assigned to egg farms. Assigning different trucks to the egg farms and different egg farms to the chicken farms generates different costs and consumes different resources. The distance and the idle space in the truck have to be minimized, while constraints such as the minimum number of chickens needed for all egg farms and the longest time that chickens can be in the truck remain. This makes the problem a special case of the multistage assignment (S-MSA) problem. A mathematical model representing the problem was developed and solved to optimality using Lingo v.11 optimization software. Lingo v.11 can solve to optimality only small- and medium-sized test instances. To solve large-sized test instances, the differential evolution (DE) algorithm was designed. An excellent decoding method was developed to increase the search performance of DE. The proposed algorithm was tested with three randomly generated datasets (small, medium, and large test instances) and one real case study. Each dataset is composed of 12 problems, therefore we tested with 37 instances, including the case study. The results show that for small- and medium-sized test instances, DE has 0.03% and 0.05% higher cost than Lingo v.11. For large test instances, DE has 3.52% lower cost than Lingo v.11. Lingo v.11 uses an average computation time of 5.8, 103, and 4320 s for small, medium and large test instances, while DE uses 0.86, 1.68, and 8.79 s, which is, at most, 491 times less than Lingo v.11. Therefore, the proposed heuristics are an effective algorithm that can find a good solution while using less computation time.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

A binary differential evolution algorithm for airline revenue management: a case study

2020 ◽  
Vol 24 (18) ◽  
pp. 14221-14234
Author(s):  
Amir Karbassi Yazdi ◽  
Mohamad Amin Kaviani ◽  
Thomas Hanne ◽  
Andres Ramos
Keyword(s):  
Differential Evolution ◽  
Revenue Management ◽  
Differential Evolution Algorithm ◽  
Airline Revenue Management ◽  
Evolution Algorithm ◽  
Binary Differential Evolution

scholarly journals Optimization Of Raw Material Inventory Costs In The Food Supply Chain Using Differential Evolution Algorithm

2018 ◽  
Vol 73 ◽  
pp. 13016
Author(s):  
Mara Huriga Priymasiwi ◽  
Mustafid
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Food Industry ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Raw Material ◽  
Inventory Cost ◽  
De Algorithm ◽  
Material Requirement ◽  
Total Inventory

The management of raw material inventory is used to overcome the problems occuring especially in the food industry to achieve effectiveness, timeliness, and high service levels which are contrary to the problem of effectiveness and cost efficiency. The inventory control system is built to achieve the optimization of raw material inventory cost in the supply chain in food industry. This research represents Differential Evolution (DE) algorithm as optimization method by minimizing total inventory based on amount of raw material requirement, purchasing cost, saefty stock and reorder time. With the population size, the parameters of mutation control, crossover parameters and the number of iterations respectively 80, 0.8, 0.5, 200. With the amount of safety stock at the company 7213.95 obtained a total inventory cost decrease of 39.95%. Result indicate that the use of DE algorithm help providein efficient amount, time and cost.

Differential Evolution Algorithm with Triangular Adaptive Control Parameter for SHEPWM Switching Pattern Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 1381
Author(s):  
Ismail Yusuf ◽  
Ayong Hiendro ◽  
F. Trias Pontia Wigyarianto ◽  
Kho Hie Khwee
Keyword(s):  
Adaptive Control ◽  
Differential Evolution ◽  
Control Parameter ◽  
Pulse Width Modulation ◽  
Differential Evolution Algorithm ◽  
De Algorithm ◽  
Harmonic Elimination ◽  
Switching Patterns ◽  
Convergence Process ◽  
Speed Up

Differential evolution (DE) algorithm has been applied as a powerful tool to find optimum switching angles for selective harmonic elimination pulse width modulation (SHEPWM) inverters. However, the DE’s performace is very dependent on its control parameters. Conventional DE generally uses either trial and error mechanism or tuning technique to determine appropriate values of the control paramaters. The disadvantage of this process is that it is very time comsuming. In this paper, an adaptive control parameter is proposed in order to speed up the DE algorithm in optimizing SHEPWM switching angles precisely. The proposed adaptive control parameter is proven to enhance the convergence process of the DE algorithm without requiring initial guesses. The results for both negative and positive modulation index (<em>M</em>) also indicate that the proposed adaptive DE is superior to the conventional DE in generating SHEPWM switching patterns

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Ali Khater Mohamed ◽  
Ehab Z. Elfeky ◽  
Mohamed Saleh
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Test Problems ◽  
New Mutation ◽  
De Algorithm ◽  
Engineering Problems ◽  
Evolution Algorithm ◽  
Mutation Scheme ◽  
Exploration Exploitation

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

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