Decomposition for Large-scale Optimization Problems with Overlapping Components

In this paper we use a divide-and-conquer approach to tackle large-scale optimization problems with overlapping components. Decomposition for an overlapping problem is challenging as its components depend on one another. The existing decomposition methods typically assign all the linked decision variables into one group, thus cannot reduce the original problem size. To address this issue we modify the Recursive Differential Grouping (RDG) method to decompose overlapping problems, by breaking the linkage at variables shared by multiple components. To evaluate the efficacy of our method, we extend two existing overlapping benchmark problems considering various level of overlap. Experimental results show that our method can greatly improve the search ability of an optimization algorithm via divide-and-conquer, and outperforms RDG, random decomposition as well as other state-of-the-art methods. We further evaluate our method using the CEC'2013 benchmark problems and show that our method is very competitive when equipped with a component optimizer

CBCC3 — A contribution-based cooperative co-evolutionary algorithm with improved exploration/exploitation balance

Cooperative Co-evolution (CC) is a promising framework for solving large-scale optimization problems. However, the round-robin strategy of CC is not an efficient way of allocating the available computational resources to components of imbalanced functions. The imbalance problem happens when the components of a partially separable function have non-uniform contributions to the overall objective value. Contribution-Based Cooperative Co-evolution (CBCC) is a variant of CC that allocates the available computational resources to the individual components based on their contributions. CBCC variants (CBCC1 and CBCC2) have shown better performance than the standard CC in a variety of cases. In this paper, we show that over-exploration and over-exploitation are two major sources of performance loss in the existing CBCC variants. On that basis, we propose a new contribution-based algorithm that maintains a better balance between exploration and exploitation. The empirical results show that the new algorithm is superior to its predecessors as well as the standard CC

Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems

This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms

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

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

Sulfur-Modulated Tin Sites Enable Highly Selective Electrochemical Reduction of CO2 to Formate

Electrochemical reduction of carbon dioxide (CO2RR) to formate provides an avenue to the synthesis of value-added carbon-based fuels and feedstocks powered using renewable electricity. Here, we hypothesized that the presence of sulfur atoms in the catalyst surface could promote undercoordinated sites, and thereby improve the electrochemical reduction of CO2 to formate. We explored, using density functional theory, how the incorporation of sulfur into tin may favor formate generation. We used atomic layer deposition of SnSx followed by a reduction process to synthesize sulfur-modulated tin (Sn(S)) catalysts. X-ray absorption near-edge structure (XANES) studies reveal higher oxidation states in Sn(S) compared with that of tin in Sn nanoparticles. Sn(S)/Au accelerates CO2RR at geometric current densities of 55 mA cm−2 at −0.75 V versus reversible hydrogen electrode with a Faradaic efficiency of 93%. Furthermore, Sn(S) catalysts show excellent stability without deactivation (<2% productivity change) following more than 40 hours of operation. With rapid advances in the efficient and cost-effective conversion of sunlight to electrical power, the development of storage technologies for renewable energy is even more urgent. Using renewable electricity to convert CO2 into formate simultaneously addresses the need for storage of intermittent renewable energy sources and the need to reduce greenhouse gas emissions. We report an increase of greater than 4-fold in the current density (hence the rate of reaction) in formate electrosynthesis compared with relevant controls. Our catalysts also show excellent stability without deactivation (<2% productivity change) following more than 40 hours of operation. The electrochemical reduction of carbon dioxide (CO2RR) offers a compelling route to energy storage and high-value chemical manufacture. The presence of sulfur atoms in catalyst surfaces promotes undercoordinated sites, thereby improving the electrochemical reduction of CO2 to formate. The resulting sulfur-modulated tin catalysts accelerate CO2RR at geometric current densities of 55 mA cm−2 at −0.75 V versus RHE with a Faradaic efficiency of 93%

Highly acid-durable carbon coated Co3O4 nanoarrays as efficient oxygen evolution electrocatalysts

Most oxygen evolution reaction (OER) electrocatalysts are not stable in corrosive acids. Even the expensive RuO2 or IrO2, the most acid-resistant oxides, can be dissolved at an oxidative potential. Herein, we realize that the failures of OER catalysts are mostly caused by the weak interface between catalysts and the substrates. Hence, the study of the interface structure between catalysts and substrates is critical. In this work, we observe that the cheap OER catalysts Co3O4 can be more durable than the state-of-the-art RuO2 if the interface quality is good enough. The Co3O4 nanosheets deposited on carbon paper (Co3O4/CP) is prepared by electroplating of Co-species and followed by a two-step calcination process. The 1st step occurs in vacuum in order to maintain the surface integrity of the carbon paper and converts Co-species to Co(II)O. The 2nd step is a calcination in ambient conditions which enables the complete transformation of Co(II)O to Co3O4 without degrading the mechanical strength of the Co3O4-CP interface. Equally important, an in situ formation of a layer of amorphous carbon on top of Co3O4 further enhances the OER catalyst stability. Therefore, these key advances make the Co3O4 catalyst highly active toward the OER in 0.5 M H2SO4 with a small overpotential (370 mV), to reach 10 mA/cm2. The observed long lifetime for 86.8 h at a constant current density of 100 mA/cm2, is among the best of the reported in literature so far, even longer than the state-of-art RuO2 on CP. Overall, our study provides a new insight and methodology for the construction of a high-performance and high stability OER electrocatalysts in corrosive acidic environments

Isochondodendrine and 2[prime or minute]-norcocsuline: additional alkaloids from Triclisia subcordata induce cytotoxicity and apoptosis in ovarian cancer cell lines

Triclisia subcordata Oliv (Menispermeaceae) is used in herbal medicine for the treatment of cancer and other diseases in Africa. This study aims to isolate minor alkaloids present in this plant and assay their cytotoxic activities. Isochondodendrine and 2[prime or minute]-norcocsuline as two minor alkaloids together with the abundant cycleanine were isolated and identified by mass spectrometry and NMR spectroscopy. Both isochondodendrine and 2[prime or minute]-norcocsuline exhibited in vitro cytotoxicity in four ovarian cancer cell lines (A2780, IGROV-1, OVCAR-8, and OVCAR-4) with IC50 ranges of 3.5-17 [small mu ]M and 0.8-6.2 [small mu ]M respectively. These alkaloids showed mostly slightly weaker potencies when tested using normal human ovarian epithelial cells, IC50 = 10.5 +/- 1.2 [small mu ]M and 8.0 +/- 0.2 [small mu ]M for isochondodendrine and 2[prime or minute]-norcocsuline, respectively. The alkaloids induced apoptosis in ovarian cancer cells because they activated caspases 3/7, induced cleavage of PARP, increased the subG1 population in cell cycle analysis and increased Annexin V/propidium iodide staining. These observations suggest that isochondodendrine and 2[prime or minute]-norcocsuline contributing to the cytotoxic activity of T. subcordata may be suitable starting points for the future development of novel therapeutics to treat ovarian cancer

Hierarchical multi-scale models for mechanical response prediction of highly filled elastic–plastic and viscoplastic particulate composites

Though a vast amount of literature can be found on modelling particulate reinforced composites and suspensions, the treatment of such materials at very high volume fractions (>90%), typical of high performance energetic materials, remains a challenge. The latter is due to the very wide particle size distribution needed to reach such a high value of In order to meet this challenge, multiscale models that can treat the presence of particles at various scales are needed. This study presents a novel hierarchical multiscale method for predicting the effective properties of elasto-viscoplastic polymeric composites at high . Firstly, simulated microstructures with randomly packed spherical inclusions in a polymeric matrix were generated. Homogenised properties predicted using the finite element (FE) method were then iteratively passed in a hierarchical multi-scale manner as modified matrix properties until the desired filler was achieved. The validated hierarchical model was then applied to a real composite with microstructures reconstructed from image scan data, incorporating cohesive elements to predict debonding of the filler particles and subsequent catastrophic failure. The predicted behaviour was compared to data from uniaxial tensile tests. Our method is applicable to the prediction of mechanical behaviour of any highly filled composite with a non-linear matrix, arbitrary particle filler shape and a large particle size distribution, surpassing limitations of traditional analytical models and other published computational models

A review of population-based metaheuristics for large-scale black-box global optimization: Part A

Scalability of optimization algorithms is a major challenge in coping with the ever growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part of the series covers two major algorithmic approaches to large-scale global optimization: problem decomposition and memetic algorithms. Part of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally touches upon the pitfalls and challenges of current research and identifies several potential areas for future research