scholarly journals Modified Differential Evolution Algorithm for a Transportation Software Application

2019 ◽  
Vol 5 (4) ◽  
pp. 84
Author(s):  
Supattananon ◽  
Akararungruangkul
Keyword(s):  
Differential Evolution ◽  
Real Time ◽  
Web Application ◽  
Transportation Problem ◽  
Selection Process ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Raw Material ◽  
Production Plant ◽  
Solution Approach

This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

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.

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.

scholarly journals Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

2017 ◽  
Vol 24 (s3) ◽  
pp. 65-71
Author(s):  
Jianjun Li ◽  
Ru Bo Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Artificial Bee Colony ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Distributed Task Allocation

Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance.

scholarly journals Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yuehe Zhu ◽  
Hua Wang ◽  
Jin Zhang
Keyword(s):  
Differential Evolution ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Local Optimum ◽  
Local Optima ◽  
Trial Vector ◽  
Mutation Strategies

Since most spacecraft multiple-impulse trajectory optimization problems are complex multimodal problems with boundary constraint, finding the global optimal solution based on the traditional differential evolution (DE) algorithms becomes so difficult due to the deception of many local optima and the probable existence of a bias towards suboptimal solution. In order to overcome this issue and enhance the global searching ability, an improved DE algorithm with combined mutation strategies and boundary-handling schemes is proposed. In the first stage, multiple mutation strategies are utilized, and each strategy creates a mutant vector. In the second stage, multiple boundary-handling schemes are used to simultaneously address the same infeasible trial vector. Two typical spacecraft multiple-impulse trajectory optimization problems are studied and optimized using the proposed DE method. The experimental results demonstrate that the proposed DE method efficiently overcomes the problem created by the convergence to a local optimum and obtains the global optimum with a higher reliability and convergence rate compared with some other popular evolutionary methods.

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 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.

scholarly journals An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

2018 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Ayda Emdadian ◽  
S. G. Ponnambalam ◽  
G. Kanagaraj
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Network Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Value Added ◽  
Raw Material ◽  
Supply Chain Network ◽  
De Algorithm ◽  
Chain Problem

In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO.

A Hybrid Differential Evolution Scheduling Algorithm to Heterogeneous Distributed System

2014 ◽  
Vol 631-632 ◽  
pp. 271-275
Author(s):  
Yan Kang ◽  
Zhong Min Wang ◽  
Ying Lin ◽  
Xiang Yun Guo
Keyword(s):  
Differential Evolution ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Greedy Heuristic ◽  
Particle Swarm Algorithm ◽  
Scheduling Problem ◽  
Heuristic Strategy

This paper presents a differential evolution algorithm with designed greedy heuristic strategy to solve the task scheduling problem. The static task scheduling problem is NP-complete and is a critic issue in parallel and distributed computing environment. A vector consists of a task permutation assigned to each individual in the target population by using DE mutation and crossover operators. A heuristic strategy is used to generate the feasible solutions as there a lot of infeasible solutions in the solution space as the size of the problem increase. And the strategies of the particle swarm algorithm are employed to modify the DE crossover operator for speeding up the search to optimal solution. And then, the individual is replaced with the corresponding target individual if it is global best or local best in terms of fitness. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms. The performances of the proposed algorithms are tested on the benchmark and compared to the best-known solutions available. The computational results demonstrate that effectively and efficiency of the presented algorithm.

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