Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

/pdf/classification-of-covid-19-patients-from-chest-ct-images-2bv5ksmqxo.pdf

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

Advances in Planar Lipid Bilayers and Liposomes

Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the best overall solution to the problem. Hence, it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to efficiently allocate computational resources among the subpopulations according to their dynamic contributions to the improvement of the objective value of the best overall solution. Our experimental results suggest that CCFR can make efficient use of computational resources and is a highly competitive CCFR for solving large-scale optimization problems.

/pdf/efficient-resource-allocation-in-cooperative-co-evolution-1emhp5k6i7.pdf

Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas. In this paper, an event-triggered impulsive (ETI) control scheme is introduced to improve the performance of DE. Impulsive control (IPC), the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). By introducing IPC and ETM into DE, we hope to change the search performance of the population in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the IPC operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of IPC, two types of impulses are presented within the framework of DE in this paper: 1) stabilizing impulses and 2) destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated. In addition, the proposed ETI is flexible to be incorporated into several state-of-the-art DE variants. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions exhibit that the developed scheme is simple yet effective, which significantly improves the performance of the considered DE algorithms.

Differential Evolution With Event-Triggered Impulsive Control

Differential Evolution (DE) is an algorithm to solve possibly nonlinear and non-differentiable global optimization problems. Classical Differential Evolution (CDE) employs a synchronous generation-based evolution strategy. We propose a modification of the CDE algorithm by incorporating mutation, crossover and selection operations into an asynchronous strategy. A novel Asynchronous Differential Evolution (ADE) is well suited for parallel optimization. Moreover even in the sequential mode its rate of convergence is competitive to CDE. The performance of the Asynchronous Differential Evolution is reported on a set of benchmark functions.

Asynchronous differential evolution

Differential evolution (DE) is an efficient algorithm to solve global optimization problems. It has a simple internal structure and uses a few control parameters. In this paper we incorporate crossover based on adaptive correlation matrix into Asynchronous differential evolution (ADE). Thanks to the proposed crossover the novel algorithm automatically adapts to the landscape of the optimized objective function. Combined with an adaptive scheme for the mutation scale factor and an automatic inflation of the population size this results in quasi parameter-free algorithm from the user's point of view. The performance of the Asynchronous differential evolution with adaptive correlation matrix is reported on the set of BBOB-2012 benchmark functions.

Asynchronous differential evolution with adaptive correlation matrix

Asynchronous Differential Evolution ADE [1] is a derivative-free method to solve global optimization problems. It provides effective parallel realization. In this work we derive ADE with restart ADE-R. By increasing population size after each restart, new strategy enhances its chances to locate the global minimum. The ADE-R algorithm has convergence rate comparable or better than ADE with fixed population sizes. Performance of the ADE-R algorithm is demonstrated on a set of benchmark functions.

Asynchronous Differential Evolution with Restart

The structure of a polydispersed population of unilamellar dimyristoylphosphatidylcholine (DMPC) vesicles in sucrose solutions has been investigated by small-angle neutron scattering (SANS) and small-angle X-ray scattering (SAXS). Calculations within the model of separated form factors (SFF) show that the structure of the vesicle system depends strongly on the sucrose concentration.

Investigation of the structure of unilamellar dimyristoylphosphatidylcholine vesicles in aqueous sucrose solutions by small-angle neutron and X-ray scattering

Application of small-angle X-ray scattering to the characterization and quantification of the drug transport nanosystem based on the soybean phosphatidylcholine.

E. I. Zhabitskaya

Papers

Asynchronous differential evolution

Asynchronous differential evolution with adaptive correlation matrix

Asynchronous Differential Evolution with Restart

Investigation of the structure of unilamellar dimyristoylphosphatidylcholine vesicles in aqueous sucrose solutions by small-angle neutron and X-ray scattering

Application of small-angle X-ray scattering to the characterization and quantification of the drug transport nanosystem based on the soybean phosphatidylcholine.