The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.

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Differential Evolution with Population and Strategy Parameter Adaptation

Mathematical Problems in Engineering ◽

10.1155/2015/287607 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 11

Author(s):

V. Gonuguntla ◽

R. Mallipeddi ◽

Kalyana C. Veluvolu

Keyword(s):

Differential Evolution ◽

Population Size ◽

Optimization Problems ◽

Search Space ◽

Benchmark Problems ◽

Parameter Adaptation ◽

De Algorithm ◽

Probabilistic Technique ◽

Number Of Individuals ◽

Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

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Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization

SLAS TECHNOLOGY Translating Life Sciences Innovation ◽

10.1177/2472630317690318 ◽

2017 ◽

Vol 22 (3) ◽

pp. 289-305 ◽

Cited By ~ 4

Author(s):

Ieong Wong ◽

Wenjia Liu ◽

Chih-Ming Ho ◽

Xianting Ding

Keyword(s):

Differential Evolution ◽

Population Size ◽

Drug Combination ◽

Evolutionary Process ◽

Effective Population ◽

Combination Optimization ◽

De Algorithm ◽

Population Reduction ◽

Optimal Drug ◽

Self Adaptive

Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction–based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.

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Global maximum power point tracking of PV system using dynamic population size differential evolution (DynNP-DE) algorithm

2014 IEEE Conference on Energy Conversion (CENCON) ◽

10.1109/cencon.2014.6967511 ◽

2014 ◽

Cited By ~ 2

Author(s):

M. F. N. Tajuddin ◽

S. M. Ayob ◽

Z. Salam

Keyword(s):

Differential Evolution ◽

Population Size ◽

Global Maximum ◽

Pv System ◽

Maximum Power Point ◽

Point Tracking ◽

De Algorithm ◽

Power Point Tracking ◽

Power Point ◽

Dynamic Population

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SWARM DIRECTIONS EMBEDDED DIFFERENTIAL EVOLUTION FOR FASTER CONVERGENCE OF GLOBAL OPTIMIZATION PROBLEMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213012400131 ◽

2012 ◽

Vol 21 (03) ◽

pp. 1240013 ◽

Cited By ~ 7

Author(s):

MUSRRAT ALI ◽

MILLIE PANT ◽

AJITH ABRAHAM ◽

CHANG WOOK AHN

Keyword(s):

Global Optimization ◽

Differential Evolution ◽

Optimization Problems ◽

Mechanical Design ◽

Empirical Studies ◽

Real Life ◽

Search Space ◽

Benchmark Problems ◽

De Algorithm ◽

Information Sharing Mechanism

In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algorithm. This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence. The embedding of swarm directions to the basic DE algorithm is done with the help of a "switchover constant" called α which keeps a record of the contraction of search space. The proposed HDE algorithm is tested on a set of 10 unconstrained benchmark problems and four constrained real life, mechanical design problems. Empirical studies show that the proposed scheme helps in improving the convergence rate of the basic DE algorithm without compromising with the quality of solution.

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Improved ensemble of differential evolution variants

PLoS ONE ◽

10.1371/journal.pone.0256206 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256206

Author(s):

Juan Yao ◽

Zhe Chen ◽

Zhenling Liu

Keyword(s):

Differential Evolution ◽

Population Size ◽

State Of The Art ◽

Population Based ◽

Size Reduction ◽

Real Parameter ◽

De Algorithm ◽

External Archive ◽

Single Objective ◽

Event Triggered

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.

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Abstract #203 The Real Life Effectiveness; Metabolic Effects and Tolerability of Hydroxychloroquine Once Daily in the Treatment of Type-2 Diabetes Mellitus

Endocrine Practice ◽

10.1016/s1530-891x(20)47049-1 ◽

2018 ◽

Vol 24 ◽

pp. 28-29

Author(s):

Hemant Thacker ◽

Rupal Shrimanker ◽

Banshi Saboo ◽

Diabetologist ◽

Anuj Maheshwari ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Real Life ◽

Metabolic Effects ◽

Life Effectiveness ◽

Once Daily ◽

The Real

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Exploring Citizenship: Unit 4 My Heritage

EDIS ◽

10.32473/edis-4h019-2019 ◽

2019 ◽

Vol 2019 (5) ◽

pp. 14

Author(s):

John Rutledge ◽

Joy C. Jordan ◽

Dale W. Pracht

Keyword(s):

Youth Development ◽

Real Life ◽

Development Program ◽

The Real ◽

Volunteer Leaders ◽

Youth Development Program ◽

Major Revision ◽

The World ◽

Individual Needs

The 4-H Citizenship Project offers the opportunity to help 4-H members relate all of their 4-H projects and experiences to the world around them. The 4-H Citizenship manuals will serve as a guide for 4-H Citizenship experiences. To be truly meaningful to the real-life needs and interests of your group, the contribution of volunteer leaders is essential. Each person, neighborhood, and community has individual needs that you can help your group identify. This 14-page major revision of Unit IV covers the heritage project. Written by John Rutledge, Joy C. Jordan, and Dale Pracht and published by the UF/IFAS Extension 4-H Youth Development program. https://edis.ifas.ufl.edu/4h019

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L'esperienza clinica con Levometadone nel trattamento del disturbo da uso di oppiacei

MISSION ◽

10.3280/mis52-2018oa8541 ◽

2019 ◽

pp. 54-57

Author(s):

Marco Riglietta ◽

Paolo Donadoni ◽

Grazia Carbone ◽

Caterina Pisoni ◽

Franca Colombi ◽

...

Keyword(s):

Adverse Events ◽

Real Life ◽

Opioid Use Disorder ◽

Racemic Mixture ◽

Opioid Use ◽

The Real ◽

New Formulation

In Italy, at the end of the 1970s, methadone hydrochloride was introduced for the treatment of opioid use disorder, in the form of a racemic mixture consisting of levomethadone and dextromethadone.In 2015 Levometadone was introduced, a new formulation marketed in Italy for the treatment of opioid use disorder in 2015.The article aims to bring the experience of an Italian Addiction Centre back to the use of this new formulation in the "real life" analyzing the efficacy, the trend of adverse events and pharmacological iterations in a context in which the treated population often uses besides the opiates, cocaine and alcohol, are burdened by a relevant physical and psychic comorbidity and frequently have a prescribed polypharmacy.

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