A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades:Part B

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

A survey of evolutionary continuous dynamic optimization over two decades – part A

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out

Evolutionary Dynamic Optimization Laboratory: A MATLAB Optimization Platform for Education and Experimentation in Dynamic Environments

Many real-world optimization problems possess dynamic characteristics. Evolutionary dynamic optimization algorithms (EDOAs) aim to tackle the challenges associated with dynamic optimization problems. Looking at the existing works, the results reported for a given EDOA can sometimes be considerably different. This issue occurs because the source codes of many EDOAs, which are usually very complex algorithms, have not been made publicly available. Indeed, the complexity of components and mechanisms used in many EDOAs makes their re-implementation error-prone. In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform also contains an education module that can be used for educational purposes. In the education module, the user can observe a) a 2-dimensional problem space and how its morphology changes after each environmental change, b) the behaviors of individuals over time, and c) how the EDOA reacts to environmental changes and tries to track the moving optimum. In addition to being useful for research and education purposes, EDOLAB can also be used by practitioners to solve their real-world problems. The current version of EDOLAB includes 25 EDOAs and three fully-parametric benchmark generators. The MATLAB source code for EDOLAB is publicly available and can be accessed from [https://github.com/EDOLAB-platform/EDOLAB-MATLAB].Comment: This work was submitted to ACM Transactions on Mathematical Software on December 7, 202

Strength characterisation of shale using Mohr-Coulomb and Hoek-Brown criteria

Parameters on rock material strengths like triaxial compressive strength are usually determined from laboratory test on intact rock samples. Uncertainties arise in predicting the behaviour of a rock mass under confinement due to its discontinuous nature. Discontinuity such as joint induces inhomogeneous and anisotropic behaviour in the rock mass, in contrast to the behaviour of intact rock samples used in the lab tests. Several empirical approaches such as Rock Mass Rating (RMR) are available to classify and to evaluate the mass strength of discontinuous rock. However, RMR suffers from several limitations for it is not suitable for very poor quality rock mass such as shale. This study investigates the suitability of the new empirical approach namely Hoek-Brown failure criterion (2002). Such that it together with RocLab software, are used to evaluate and to assess the strength of rock mass under confinement and field condition. In this study two failure criteria were served. Results obtained indicate that the failure envelope derived using the new Hoek-Brown criterion shows a better presentation of shale under field condition in comparison with the classic Mohr-Coulomb method

Robust optimization over time : a critical review

Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system’s inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive review of them. This survey also reviews ROOT benchmarks and performance indicators. Finally, we identify several future research directions

Vitamin D Levels in Asymptomatic Adults-A Population Survey in Karachi, Pakistan

Background: It is well established that low levels of 25(OH) Vitamin D (/dL) are a common finding world over, affecting over a billion of the global population. Our primary objective was to determine the prevalence of vitamin D deficiency and insufficiency in the asymptomatic adult population of Karachi, Pakistan and the demographic, nutritional and co-morbidity characteristics associated with serum vitamin D levels. Methods: A cross-sectional population survey was conducted at two spaced out densely populated areas of the city. Serum levels of 25OH vitamin D were measured and GFR as renal function was assessed by using 4 variable MDRD formula. Results: Our sample of 300 had a median age of 48(interquartile range 38-55) years. The median level of serum vitamin D was 18.8 (IQ range 12.65-24.62) ng/dL. A total of 253 (84.3%) respondents had low levels (/dL) of 25OH vitamin D. Serum PTH and vitamin D were negatively correlated (r = -0.176, p = 0.001). The median PTH in the vitamin D sufficiency group was 38.4 (IQ range28.0-48.8)pg/mL compared with 44.4 (IQ range 34.3-56.8) pg/mL in the deficiency group (p = 0.011).The median serum calcium level in the sample was 9.46(IQ range 9.18-9.68) ng/dL. Low serum levels of vitamin D were not associated with hypertension (p = 0.771) or with an elevated spot blood pressure (p = 0.164).In our sample 75(26%) respondents had an eGFR corresponding to stage 2 and stage 3 CKD. There was no significant correlation between levels of vitamin D and eGFR (r = -0.127, p-value = 0.277). Respondents using daily vitamin D supplements had higher 25 OH vitamin D levels (p-value = 0.021). Conclusion: We observed a high proportion of the asymptomatic adult population having low levels of vitamin D and subclinical deterioration of eGFR. The specific cause(s) for this observed high prevalence of low 25OH vitamin D levels are not clear and need to be investigated further upon

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades - Part A

Yazdani D, Cheng R, Yazdani D, Branke J, Jin Y, Yao X. A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades - Part A. IEEE Transactions on Evolutionary Computation. 2021;25(4):609-629.Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part article, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms (DOAs), namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multipopulation methods, which are underrepresented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an in-depth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the DOAs, and dynamic real-world applications. Finally, several opportunities for future work are pointed out