MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems

2020 ◽  
Vol 97 ◽  
pp. 106761 ◽  
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Shokooh Taghian ◽  
Seyedali Mirjalili ◽  
Hossam Faris
Keyword(s):  
Differential Evolution ◽  
Engineering Design ◽  
Differential Evolution Algorithm ◽  
Design Problems ◽  
Trial Vector ◽  
Engineering Design Problems ◽  
Evolution Algorithm

P-lsGOF: A parallel learning-selection-based global optimization framework

2020 ◽  
Vol 39 (5) ◽  
pp. 7333-7361
Author(s):  
Mingcheng Zuo ◽  
Guangming Dai
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Principal Component ◽  
Parallel Learning ◽  
Optimization Framework ◽  
Engineering Design Problems ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

When optimizing complicated engineering design problems, the search spaces are usually extremely nonlinear, leading to the great difficulty of finding optima. To deal with this challenge, this paper introduces a parallel learning-selection-based global optimization framework (P-lsGOF), which can divide the global search space to numbers of sub-spaces along the variables learned from the principal component analysis. The core search algorithm, named memory-based adaptive differential evolution algorithm (MADE), is parallel implemented in all sub-spaces. MADE is an adaptive differential evolution algorithm with the selective memory supplement and shielding of successful control parameters. The efficiency of MADE on CEC2017 unconstrained problems and CEC2011 real-world problems is illustrated by comparing with recently published state-of-the-art variants of success-history based adaptative differential evolution algorithm with linear population size reduction (L-SHADE) The performance of P-lsGOF on CEC2011 problems shows that the optimized results by individually conducting MADE can be further improved.

scholarly journals Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

2019 ◽  
Vol 1 (1) ◽  
pp. 75-93 ◽  
Author(s):  
Peerawat Chokanat ◽  
Rapeepan Pitakaso ◽  
Kanchana Sethanan
Keyword(s):  
Differential Evolution ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Differential Evolution Algorithm ◽  
Transportation Cost ◽  
Raw Milk ◽  
Routing Problem ◽  
Trial Vector ◽  
Evolution Algorithm ◽  
Special Case

This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks’ and tanks’ cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.

Experimental Study on Recent Advances in Differential Evolution Algorithm

2013 ◽  
pp. 111-133
Author(s):  
G. Jeyakumar ◽  
C. Shanmugavelayutham
Keyword(s):  
Experimental Study ◽  
Differential Evolution ◽  
Performance Enhancement ◽  
Differential Evolution Algorithm ◽  
Competitive Performance ◽  
Trial Vector ◽  
Evolution Algorithm ◽  
Objective Function Values ◽  
The Given ◽  
Mixed Variant

The Differential Evolution (DE) is a well known Evolutionary Algorithm (EA), and is popular for its simplicity. Several novelties have been proposed in research to enhance the performance of DE. This paper focuses on demonstrating the performance enhancement of DE by implementing some of the recent ideas in DE’s research viz. Dynamic Differential Evolution (dDE), Multiple Trial Vector Differential Evolution (mtvDE), Mixed Variant Differential Evolution (mvDE), Best Trial Vector Differential Evolution (btvDE), Distributed Differential Evolution (diDE) and their combinations. The authors have chosen fourteen variants of DE and six benchmark functions with different modality viz. Unimodal Separable, Unimodal Nonseparable, Multimodal Separable, and Multimodal Nonseparable. On analyzing distributed DE and mixed variant DE, a novel mixed-variant distributed DE is proposed whereby the subpopulations (islands) employ different DE variants to cooperatively solve the given problem. The competitive performance of mixed-variant distributed DE on the chosen problem is also demonstrated. The variants are well compared by their mean objective function values and probability of convergence.

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