Abstract Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including M any-dimensions, M any-changes, M any-optima, M any-constraints, and M any-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty , increasing algorithm diversity , accelerating convergence speed , reducing running time , and extending application field . Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.
The raising and flowing of groundwater caused by coal mining threaten the stability of mining faces, which cause casualties and machine damage accidents. Among the above accidents, the water inrush disaster caused by the water-rich water-conducting fault zone is the largest. Considering the complexity of geological structure and the suddenness of water inrush, reserving a reasonable thickness of waterproof coal pillars in front of the fault tectonic belt can effectively predict and control the occurrence of water inrush. The excellent adaptability of the numerical model to the geological conditions makes it an effective research method for simulating waterproof coal pillars. Based on the analysis of the background of on-site mining, this paper proposes a three-zone waterproof coal pillar calculation theory and establishes a numerical model for comparative analysis. The comparison results show that (1) the elastic-plastic theory and fracture theory can be used to calculate the thickness of the disturbed zone and the water-resisting zone, and the thickness of the fractured zone is positively correlated with the accuracy of the existing detection technology and equipment. (2) For the numerical model results, the increase of tangential stress is positively correlated with the distance of coal seam mining and the thickness of fault; the large plastic zone of the fault causes a higher increase in pore pressure, which ultimately increases the risk of water inrush. (3) The two results are in good agreement. The theoretical results have a safety margin, indicating that the three-zone theory is reasonable, which are used to guide the actual mining of the project to ensure the smooth passage of the project through the fault area.
Based on the analysis of SaaS's features, an evaluation indicator system for evaluating SaaS in enterprise was proposed. Then with the rough set theory, the evaluation model and evaluation of the steps was built. Through empirical analysis shows that the evaluation model and the evaluation method can help enterprise implement the evaluation of Saas impersonal.
Existing differential evolution (DE) algorithms often face two challenges. The first is that the optimization performance is significantly affected by the ad hoc configurations of operators and parameters for different problems. The second is the long runtime for real-world problems whose fitness evaluations are often expensive. Aiming at solving these two problems, this paper develops a novel double-layered heterogeneous DE algorithm and realizes it in cloud computing distributed environment. In the first layer, different populations with various parameters and/or operators run concurrently and adaptively migrate to deliver robust solutions by making the best use of performance differences among multiple populations. In the second layer, a set of cloud virtual machines run in parallel to evaluate fitness of corresponding populations, reducing computational costs as offered by cloud. Experimental results on a set of benchmark problems with different search requirements and a case study with expensive design evaluations have shown that the proposed algorithm offers generally improved performance and reduced computational time, compared with not only conventional and a number of state-of-the-art DE variants, but also a number of other distributed DE and high-performing evolutionary algorithms. The speedup is significant especially on expensive problems, offering high potential in a broad range of real-world applications.
Abstract Aim This study was designed to evaluate the relationship between sleep quality and hypertension and to determine if there was an association between nondipper blood pressure (BP) and sleep quality in chronic kidney disease (CKD) patients. Methods A total of 775 pre‐dialysis CKD patients (314 normal BP patients, 461 hypertension patients) defined as dippers or nondippers by ambulatory BP monitoring were recruited for this study. Demographics and clinical correlates were collected, including body mass index, estimated glomerular filtration rate (eGFR) and other measures. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Results A total of 185 (58.9%) patients with normal BP and 341 (74.0%) hypertensive patients had a nondipper BP pattern. The hypertension group had a higher prevalence of the nondipper BP pattern, smoking, alcohol intake and diabetes mellitus (DM) and lower eGFR levels and poorer sleep quality than the normal BP group. Patients with the nondipper BP pattern had lower haemoglobin, worse renal function and poorer sleep quality when compared with hypertensive CKD patients with the dipping BP pattern. PSQI scores were significantly associated with the rate of nocturnal BP decline ( P < 0.05) in the hypertension group but not in the normal BP group. Poor sleep quality was an independent factor affecting BP pattern in hypertensive CKD patients using multivariate linear and logistic regression analyses. There was no association between sleep quality and hypertension in CKD patients after multivariate logistic regression analyses. Conclusion Poor sleep quality, which is commonly observed in pre‐dialysis CKD patients, is an independent associated factor of the nondipper BP pattern in hypertensive CKD patients. No association was found between poor sleep and nondipper BP in normotensive patients.
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