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This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

A feasibility level nearshore wave energy resource characterization is conducted for the East Coast of the United States using a 32-year (1979–2010) hindcast from a high-resolution unstructured-grid Simulating Waves Nearshore (SWAN) model with a spatial resolution of 200 m along the coastline. Wave energy resource attributes including wave energy potentials, seasonal variability, frequency and directional spreading, and extreme sea states are characterized using a broad range of resource parameters from which opportunities, risks, and constraints for wave energy conversion (WEC) projects are assessed. Cross-shore and alongshore variations of these parameters due to varying wave energy climate and coastline orientation relative to the dominant wave systems are examined. The present study also introduces a zero-crossing method for delineating wave energy climate regions based on a broad range of resource attributes beyond just wave power. Applying this method, eight nearshore wave energy climate regions are delineated for the East Coast; each region with a unique set of resource attributes to inform regional energy planning, WEC project development, conceptual WEC design, and the operation and maintenance of WEC projects.

Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this article, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties, such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.

In this paper, N-TiO2/Graphene/Ho2O3/Titanium bifacial nanocomposite electrode was prepared. First, the effect of Ho2O3 to N-TiO2 wt.% in the nanocomposite preparation matrix was investigated through studying visible light photoelectrocatalysis process in industrial wastewater decolorization. Characterizations of optimum catalyst were performed using DRS, XRD, SEM and EDX analyses. Then, the effect of operating variables i.e. catalyst dosage (electrode number(s)), wastewater pH, applied bias potential, visible light power and contact time on the extent of wastewater decolorization was investigated by the visible light photoelectrocatalysis process. It was observed that almost 80% wastewater decolorization was achieved using the photoelectrocatalysis at the optimum conditions by N-TiO2/Graphene/(10%) Ho2O3. Artificial neural network was used for model the photoelectrocatalysis process and determine the relative importance of investigated operating variables. It was also established that combination of photoelectrocatalysis with ozonation yielded 100% decolorization in 90 min and 96% COD removal in 270 min under the optimized conditions. Moreover, the catalyst sheet was enough stable to be used in all wastewater treatment experiments.

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.

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III&reg, model with a grid resolution of 4 arc-minutes (6&ndash, 7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200&ndash, 300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well-studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark and show its nonseparable nature irrespective of its number of peaks. We then propose a composite moving peaks benchmark suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200 dimensions, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.

Coal is the most abundant fossil fuel in the world and its combustion accompanies with the emission of SOX, which is responsible for serious environmental problems. To reduce the emission of SOX is essential for clean fuel. In the present study, indigenous microorganisms acclimatized from fat coal itself and exotic microorganisms from sewage sludge were used for coal biodesulfurization. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and Raman spectral analysis were used to investigate the structural change of fat coal during the bioleaching. Results showed that kaolinite and quartz were the main minerals, and pyrite was the main inorganic sulfide in fat coal. After 36-day biodesulfurization, the total sulfur in fat coal decreased from 5.07% to 2.77% and 2.75%, and the pyritic removal was 77.68% and 87.88% with indigenous and exotic microorganisms, respectively. The exotic microorganisms were more effective to oxidize the pyrite than the indigenous microorganisms. But both microorganisms did not interact with the organic sulfur, which existed as C S bond in fat coal. FT-IR analyses showed the damage of kaolinite structure, indicating potential microbial effect on kaolinite, which has not been reported before. Raman spectral analysis was for the first time used to investigate the changes of coal macromolecular structure during the bioleaching. FT-IR and Raman spectra confirmed the changes of aromatic C H, O H bonds, which led to the increase of carbon crystallinity index.

In this study, feasibility, optimization, and mechanisms of enzyme pretreatment to enhance anaerobic digestion of corn stover were investigated. Results showed that the enzyme pretreatment efficiently enhanced the biogas yield, and the optimal conditions of enzyme pretreatment were an enzyme load of 30 FPU/g, a pretreatment time of 24 h, and a solid content of 60 g/L. Under the optimal conditions, the cumulative biogas yield increased by 36.9%, which was mainly attributed to disruption of surface structure and degradation of noncrystalline cellulose in the enzyme-pretreated corn stover. The kinetic analysis indicated that enzyme pretreatment significantly enhanced the hydrolysis rate and biogas production rate to 0.15/d and 23.89 mL/gVS, and shortened the lag phase time to 1.2 d. Correlation analysis illustrated that the SCOD yield of 250-350 mg/g from corn stover after enzyme pretreatment was suitable for the further anaerobic digestion of corn stover.

Energy plants have several advantages, such as, low lignin, fast growth, large production and perenniality, and present great potential for biofuel production. To enhance its biodegradability, in this study, carbide slag as a novel pretreatment agent was employed to pretreat four typical energy plants, namely Medicago sativa, Panicum virgatum, Triarrhena lutarioriparia, and Arundo donax. These energy plants were all pretreated in 1.75% (w/v) carbide slag solution at 120 °C for 1 h, and then were hydrolyzed to produce reducing sugar within 72 h by an enzymes loading of 30 FPU/g substrate. The results showed that thermo-carbide slag remarkably disrupted the surface microstructure from a high compact and tight structure to a poly-ravine and distorted structure of four energy plants. Thermo-carbide slag pretreatment significantly facilitated the removal of hemicellulose and lignin, and increased the crystallinity indexes. After thermo-carbide slag pretreatment, four energy plants yielded more than 220 mg/g of reducing sugar. The maximum reducing sugar yield of 297.87 mg/g was produced from pretreated Panicum virgatum, which is 7.9 times higher compared with that of raw Panicum virgatum. The significant improvement of enzymatic hydrolysis may be attributed to the low lignin content, higher retention of cellulose, and low crystallinity after the thermo-carbide slag pretreatment. Thermo-carbide slag pretreatment is a promising pretreatment method, especially for Panicum virgatum. Carbide slag, as the abundant and cheap alkaline waste, can reduce the processing cost of energy plant bioconversion for further biofuel production. The study provides a novel research direction for further biofuel production through waste treating waste.

The Zagros forests in western Iran are a crucial source of environmental services, but are severely threatened by climatic and anthropological constraints. One crucial forest parameter is the amount of canopy coverage which enables an indirect assessment of aboveground biomass and, in turn, the carbon emission and sequestration. The aim of this study is estimation of forest crown density using Pleiades satellite data and three non-parametric classification include random forest (RF), boosted regression tree (BRT) and classification and regression tree (CART) in Kaka Reza of Lorestan province, western Iran. For this purpose, we then ensured the accuracy of geometric images, creating the necessary processing such as vegetation indices, principal component analysis and texture analysis was performed on the original bands using a random—systematic sampling design, 96 sample plots were taken. Results showed that RF compared to the two other algorithms with overall accuracy of 75% and kappa coefficient of 0.73 could better classify the forest crown density, while the CART method had the lowest accuracy with overall accuracy of 71% and kappa coefficient of 0.68. Also results showed BRT had overall accuracy of 71% and kappa coefficient of 0.68. Overall results showed Pleiades satellite data and non-parametric classification method had high capability for separation crown density in Zagros region.

Alkaline pretreatment shows a good effect on delignification of lignocellulosic biomass, and carbide slag, an alkaline industrial waste, was first applied to pretreat turfgrass pruning. Three thermo-alkaline pretreatment methods were compared to evaluate their ability to destroy the biomass structure and to enhance the enzymatic hydrolysis efficiency of turfgrass pruning, namely thermo-NaOH pretreatment, thermo-lime pretreatment, and thermo-carbide slag pretreatment. Results showed that the maximum net reducing sugar yield of 269.55 mg/g raw biomass was achieved by thermo-carbide slag pretreatment. Compared with raw biomass, the lignin and hemicellulose content of turfgrass pruning after thermo-carbide slag pretreatment reduced by 35.57% and 62.40%, respectively. SEM and FTIR analyses showed that the surface structure and chemical groups of turfgrass pruning were obviously destroyed by thermo-carbide slag pretreatment. There was a high positive correlation between the enzymatic hydrolysis efficiency and cellulose content of substrate. However, a high negative correlation was observed between the enzymatic hydrolysis efficiency and lignin content of substrate. The enzymatic hydrolysis kinetics with different pretreatments were well described by the fractal-like kinetics model, and the rate constant of enzymatic hydrolysis of thermo-alkali pretreated turfgrass pruning was about three times of that of the unpretreated turfgrass pruning. Overall, carbide slag is an ideal pretreatment material for enzymatic hydrolysis of turfgrass pruning in terms of hydrolysis efficiency and waste reuse.

A high-resolution, unstructured Simulating WAves Nearshore (SWAN) model with a resolution of 200 m within 20 km of the coast was developed to provide a reliable setting for a long-term wave energy characterization of the Gulf of Mexico, Puerto Rico, and the U.S. Virgin Islands. A thorough model parameter sensitivity analysis, as well as a calibration process for selecting the whitecapping dissipation formulation, were conducted. Sensitivity analyses for the simulation timestep and number of iterations highlighted the less-studied interplay between these parameters in SWAN, which can substantially affect simulation accuracy and cost and is vital for the next step long-term simulation of the wave energy resources. For the present study, a 3 min timestep and three iterations are optimum. The “Garden Sprinkling” effect and the cut-off frequency were also investigated. Using 36 directional bins and a larger cut-off frequency (1.0 Hz) enable the best agreement between the model simulation and the in situ wave observations. This subsequently leads to improved model skill and performance in resolving the observed International Electrotechnical Commission (IEC) wave energy resource parameters that are highly non-linear functions of wave spectral moments.

Grass clipping, a cellulose-rich raw material, has great potential to produce biofuels, but must be firstly hydrolyzed to liberate fermentable sugars. In this study, grass clipping was pretreated with ultrasound (US), Ca(OH)2, NaOH, US-Ca(OH)2 and US-NaOH at relatively low temperature to enhance its enzymatic hydrolysis. The solubilization of hemicellulose and lignin, and crystallinity index of cellulose increased after US-alkaline pretreatment, leading to a significant increase of enzyme accessibility to cellulose. Compared with another four pretreatments, US-Ca(OH)2 pretreatment of grass clipping showed the best improvement for reducing sugar yield. X-ray diffraction (XRD) determination and scanning electron microscope (SEM) observation showed that the crystallinity index of grass clipping increased and the grass clipping surface suffered from serious erosion after US-Ca(OH)2 pretreatment. Then, the operating conditions of US-Ca(OH)2 pretreatment and enzymatic hydrolysis were systematically optimized, and the suitable operating conditions were as follows: US power density of 0.65 W/ml, US pretreatment time of 30 min, Ca(OH)2 concentration of 0.75%, pretreatment temperature of 75 °C, enzyme loading of 125 FPU/g, and hydrolysis time of 72 h. The reducing sugar yield of grass clipping pretreated by US-Ca(OH)2 reached 414 mg/g, increasing by 3.5 times compared with that of raw grass clipping. The US-Ca(OH)2 pretreatment of grass clipping at low temperature significantly enhanced the potential of grass clipping as a promising raw material to produce biofuels.

In this study, physical method reinforced chemical pretreatments of Triarrhena lutarioriparia (TL) were compared to explore the higher reducing sugar yield in subsequent enzymatic hydrolysis. Four different pretreatments, namely hydrothermal pretreatment (HTP), dilute acid pretreatment (HCl-P), ultrasound assisted acid pretreatment (HCl + U) and microwave assisted acid pretreatment (HCl + M) were conducted on TL. The structural features of TL after different pretreatments were investigated by Scanning electron microscopy (SEM), X-ray powder diffraction (XRD) and Fourier transformed infrared (FT-IR) techniques. The fractal-like theory was introduced to study the enzymatic hydrolysis kinetics of TL. Results showed that the reducing sugar yield of HCl-P reached 100.14 mg/g, showing 1.34 times higher than that of the raw. Due to the hemicellulose and lignin removal and lignocellulosic structure destruction, ultrasound and microwave irradiation strengthened the HCl-P, leading to a great enhancement of enzymatic hydrolysis of TL, especially the microwave irradiation. Moreover, the fractal-like theory was confirmed to be satisfactory for studying the enzymatic hydrolysis kinetics of lignocellulosic biomass.

Purpose The purpose of this paper is to examine the level of cardinal welfare based on Sen’s index using both Pareto and non-Pareto states besides evaluating the effective factors on the welfare changes according to a fuzzy least-squares regression model. Design/methodology/approach The social welfare functions express the optimal social allocations in the public economics. Therefore, they can be applied as a tool to represent the difference of utility allocations among consumers. There are various criteria on the literature pertaining to the social welfare functions such as those of Dasgupta et al. (1970), Sheshinski (1972), Sen (1974), Yitzhaki (1979), Shorrocks (1983), Kakwani (1984), Dagum (1990, 1993), Mukhopadhaya (2003), and Mukhopadhaya and Rao (2001). The Sen’s welfare function because of strong theoretical basis and enjoying the welfare axioms is more celebrated among others so that it is a function of individual utilities resulting from the individual’s social positions. Findings The findings indicate that the welfare level has increased by about 4.8 percent during the period 2002-2007; by nearly 3.1 percent during 1997-2001 and mature to 2.7 percent from 1992 to 1996 such that the period 1997-2007 has had the highest level of social welfare improvement in Iran. The results obtained from the fuzzy regression show that the unemployment rate, inflation rate and Gini coefficient variables have an adverse relation with the cardinal welfare in both Pareto and non-Pareto cases, while the literacy rate and government expenditures have a positive relation with the welfare index. The findings also imply that there is a positive relationship between economic growth and welfare level in Iran. Originality/value I verify that this manuscript is an original study.

The performance of two methods for quantifying whitecapping dissipation incorporated in the Simulating Waves Nearshore (SWAN) wave model is evaluated for waves generated along and off the US east coast under energetic winter storms with a predominantly westerly wind. Parameterizing the whitecapping effect can be done using the Komen-type schemes, which are based on mean spectral parameters, or the saturation-based (SB) approach of van der Westhuysen (2007), which is based on local wave parameters and the saturation level concept of the wave spectrum (we use “Komen” and “Westhuysen” to denote these two approaches). Observations of wave parameters and frequency spectra at four National Data Buoy Center (NDBC) buoys are used to evaluate simulation results. Model–data comparisons show that when using the default parameters in SWAN, both Komen and Westhuysen methods underestimate wave height. Simulations of mean wave period using the Komen method agree with observations, but those using the Westhuysen method are substantially lower. Examination of source terms shows that the Westhuysen method underestimates the total energy transferred into the wave action equations, especially in the lower frequency bands that contain higher spectral energy. Several causes for this underestimation are identified. The primary reason is the difference between the wave growth conditions along the east coast during winter storms and the conditions used for the original whitecapping formula calibration. In addition, some deficiencies in simulation results are caused along the coast by the “slanting fetch” effect that adds low-frequency components to the 2-D wave spectra. These components cannot be simulated partly or entirely by available source terms (wind input, whitecapping, and quadruplet) in models and their interaction. Further, the effect of boundary layer instability that is not considered in the Komen and Westhuysen whitecapping wind input formulas may cause additional underestimation.

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.

In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt.

When dual-chamber microbial fuel cell (MFC) is used to detect biochemical oxygen demand (BOD), dissolved oxygen is traditionally used as cathodic electron acceptor. The detection limit of this MFC-based BOD biosensor is usually lower than 200 mg/L. In this paper, the startup of MFC-based BOD biosensor was researched and the external resistor of MFC was optimized. Results showed that the MFC started up with the dissolved oxygen as cathodic electron acceptor within 10 d, and the external resistor was optimized as 500 Ω to ensure the maximum output power of MFC. Dissolved oxygen and potassium permanganate (KMnO4) were used as cathodic electron acceptor to run MFC for detection of wastewater BOD, and the performances of two kinds of BOD biosensors were compared. The MFC-based BOD biosensor using KMnO4 (10 mmol/L) as cathodic electron acceptor exhibited an excellent performance, compared with that using dissolved oxygen. The upper limit of BOD detection was greatly broadened to 500 mg/L, the response time was shortened by 50% for artificial wastewater with a BOD of 100 mg/L, and the relative error of BOD detection was reduced to less than 10%. The MFC-based BOD biosensor using KMnO4 as cathodic electron acceptor showed a better linear relationship (R2 > 0.992) between the electric charge and BOD concentration within a BOD range of 25–500 mg/L. The MFC-based BOD biosensor using the KMnO4 as cathodic electron acceptor is promising with a better application prospect.

A mixed nano-metal oxides of Cu-Ni-Co has been synthesized. Several characterization techniques (EDS, XRD, TEM, and SEM) have been used to provide insight into the nature and structure of the catalyst. The size of this mixed metal oxide is 22 nm. The SEM images indicate that the sample with spherical particles, of which spherical assembly is comprised of elongated rod/needle-like subunits pointing radially outward, creates a needle ball-like structure. To efficiently catalyze the selective oxidation of sulfide towards sulfoxide, this heterogeneous catalyst uses an oxidizing agent (hydrogen peroxide- H2O2) and a solvent (acetonitrile) in mild conditions. The influence of reaction temperature and sulfide/oxidant molar ratio was evaluated with respect to sulfide conversion and chemoselectivity towards the sulfoxide product. Under optimized conditions, product yields in the range from 70 to 97% were obtained.

Biogas production via anaerobic digestion from sewage sludge have been investigated in different reactors. However, the efficient conversion of sewage sludge into biogas via continuous operational reactor with short retention time, have been overlooked. Considering the liquid-like property of sewage sludge pretreated by high pressure homogenization (HPH), in this study, the biogas production from HPH-pretreated sewage sludge was investigated in an expanded granular sludge blanket (EGSB) reactor for 219 d. Under optimized hydraulic retention time (HRT) of 6 d, upflow velocity of 4 m/h and organic loading rate (OLR) of 1.66 kg total chemical oxygen demand (TCOD)/(m3·d), the raw sludge was digested with a biogas production, methane content in biogas, TCOD and volatile solid (VS) removal of 215 mL/(gTCOD)·d, 52 %, 43 % and 35 %, respectively. After sludge HPH pretreatment, the HRT was shortened to 5 d due to the sludge disintegration of 24 % and enhanced growth of acetolastic Methanosaeta and hydrogenotrophic Methanolinea, subsequently, the biogas production, methane content in biogas, TCOD and VS removal increased to 240 mL/(gTCOD)·d, 57 %, and 58 % and 43 %, respectively. Abundance of dominant bacterial phylum Chloroflexi and archaeal genera Methanosaeta significantly increased for the improvement of organic biodegradation and methane production, respectively, reflecting the successful EGSB operation. Due to the good performances of EGSB reactor for the anaerobic digestion of HPH-pretreated sewage sludge, the combined HPH-pretreatment and EGSB reactor is promising to be applied in sustainable wastewater treatment plants.

This article proposes a competitive divide-and-conquer algorithm for solving large-scale black-box optimization problems for which there are thousands of decision variables and the algebraic models of the problems are unavailable. We focus on problems that are partially additively separable, since this type of problem can be further decomposed into a number of smaller independent subproblems. The proposed algorithm addresses two important issues in solving large-scale black-box optimization: (1) the identification of the independent subproblems without explicitly knowing the formula of the objective function and (2) the optimization of the identified black-box subproblems. First, a Global Differential Grouping (GDG) method is proposed to identify the independent subproblems. Then, a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is adopted to solve the subproblems resulting from its rotation invariance property. GDG and CMA-ES work together under the cooperative co-evolution framework. The resultant algorithm, named CC-GDG-CMAES, is then evaluated on the CEC’2010 large-scale global optimization (LSGO) benchmark functions, which have a thousand decision variables and black-box objective functions. The experimental results show that, on most test functions evaluated in this study, GDG manages to obtain an ideal partition of the index set of the decision variables, and CC-GDG-CMAES outperforms the state-of-the-art results. Moreover, the competitive performance of the well-known CMA-ES is extended from low-dimensional to high-dimensional black-box problems.

Lignocellulosic biomass is one of the most abundant renewable resources, and their transformation into bioenergy is an effective approach to alleviate energy shortage and recycle organic wastes. This review summarized the rumen microorganisms and related enzymes, and rumen digestion strategies and mechanisms for improvement of anaerobic rumen digestion in engineering application. Main related literatures were searched on sciencedirect and web of science, systematically summarized, compared and analyzed. Rumen microorganisms accomplish the hydrolysis, acidogenisis and methanogenesis of lignocellulosic biomass, which mainly depends on the synergy of microorganisms and enzymes. Rumen bacteria are the main players and anaerobic fungi play an indispensable role in the hydrolysis and acidogenesis of lignocelluloses. The interaction of different microorganisms promotes the efficiency of lignocelluloses conversion to renewable energy in rumen digestion system, especially protozoa and methanogens. Rumen microorganisms secrete the cellulases, hemicellulases and ligninases, which have their own unique structure and action mode and synergistically promote the lignocellulose degradation. Anaerobic digestion reactor cannot be simply designed through simulating the rumen structure and environment, while the simulation of rumen digestion strategies according to the physicochemical properties and microbial community is more important. The substrate structure and additives significantly improve the rumen digestion of lignocellulosic biomass as a potential design strategy. Finally, the current challenges, future works, and prospects on biological conversion of lignocellulose to bioenergy with rumen microorganisms are outlined.

The compressive strength, permeability, porosity and Young's modulus are the most important parameters affecting on the consolation of oil wells rocks. The correct prediction of these parameters depends on the type and concentration of chemical substances in them. Therefore, modeling and optimization of these parameters on the basis of the amount and concentration of chemicals (resins) are important for reducing the production of sand along with hydrocarbon fluids to the highest possible extent. In this research work, the individual and interactive effects of the amount and concentration of chemical substances were studied on compressive strength, permeability, porosity and Young's modulus for the epoxy and furan sand consolidated systems. The experimental design and optimization of the parameters were performed according to Design Expert software. The proposed models can be used to predict the above mentioned parameters in the specified ranges with an error of less than 10%. Also, the models could be employed as an optimization tool for designing epoxy and furan sand consolidation operations in the oil and gas industries. The samples made under optimum conditions with both resins have approximately mechanical strength four times higher than the limit critical sand production. The samples permeability was about 2000 md for epoxy resin and 7500 md for the furan resin at optimum conditions. On the other hand, the Young's modulus of the sample made with epoxy resin was 20% less than the furan resin sample, which showed that the resistance of the epoxy resin sample is higher than that of the furan resin against stress changes. Finally, the accuracy of the optimization and modeling of experimental data were studied through both statistical and experimental data. The accuracy of the modeling and optimization was evident in both methods.

In this paper, a hybrid genetic programming (GP) with multiple genes is implemented for developing prediction models of spectral energy demands. A multi-objective strategy is used for maximizing the accuracy and minimizing the complexity of the models. Both structural properties and earthquake characteristics are considered in prediction models of four demand parameters. Here, the earthquake records are classified based on soil type assuming that different soil classes have linear relationships in terms of GP genes. Therefore, linear regression analysis is used to connect genes for different soil types, which results in a total of sixteen prediction models. The accuracy and effectiveness of these models were assessed using different performance metrics and their performance was compared with several other models. The results indicate that not only the proposed models are simple, but also they outperform other spectral energy demand models proposed in the literature.

Problem decomposition plays an essential role in the success of cooperative co-evolution (CC), when used for solving large-scale optimization problems. The recently proposed recursive differential grouping (RDG) method has been shown to be very efficient, especially in terms of time complexity. However, it requires an appropriate parameter setting to estimate a threshold value in order to determine if two subsets of decision variables interact or not. Furthermore, using one global threshold value may be insufficient to identify variable interactions in components with different contribution to the fitness value. Inspired by the different grouping 2 (DG2) method, in this paper, we adaptively estimates a threshold value based on computational round-off errors for RDG. We derive an upper bound of the round-off errors, which is shown to be sufficient when identifying variable interactions across a wide range of large-scale benchmark problems. Comprehensive numerical experimental results showed that the proposed RDG2 method achieved higher decomposition accuracy than RDG and DG2. When embedded into a CC framework, it achieved statistically equal or significantly better solution quality than RDG and DG2, when used to solve the benchmark problems.

Dynamic optimization problems (DOPs) are problems that change over time. However, most investigations in this domain are focused on tracking moving optima (TMO) without considering the cost of switching from one solution to another when the environment changes. Robust optimization over time (ROOT) tries to address this shortcoming by finding solutions which remain acceptable for several environments. However, ROOT methods change solutions only when they become unacceptable. Indeed, TMO and ROOT are two extreme cases in the sense that in the former, the switching cost is considered zero and in the latter, it is considered very large. In this paper, we propose a new semi ROOT algorithm based on a new approach to switching cost. This algorithm changes solutions when: 1) the current solution is not acceptable and 2) the current solution is still acceptable but algorithm has found a better solution and switching is preferable despite the cost. The main objective of the proposed algorithm is to maximize the performance based on the fitness of solutions and their switching cost. The experiments are done on modified moving peaks benchmark (mMPB) and the performance of the proposed algorithm alongside state-of-the-art ROOT and TMO methods is investigated.

Rumen fluid, formed in rumen of ruminants, includes a complex microbial population of bacteria, protozoa, fungi and archaea, and has high ability to degrade lignocellulosic biomass. In this study, rumen fluid was used to ferment grass clipping for enhancing the hydrolysis and acidification of organic matters. Results showed that strict anaerobic condition, higher grass clipping content and smaller particle size of grass clipping were beneficial to the hydrolysis and acidification of organics. The increase of SCOD and total VFA concentration respectively reached 24.9 and 10.2 g/L with a suitable grass clipping content of 5%, a particle size

A proportional-integral-derivative (PID) controller is a control loop feedback mechanism widely employed in industrial control systems. The parameters tuning is a sticking point, having a great effect on the control performance of a PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a well-performing controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications. The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.

Anaerobic fermentation of biomasses for volatile fatty acid (VFA) production has attracted widespread attention. Grass biomasses have the advantages of high yield, rapid and perennial growth, low lignin content and low input, etc. In order to improve VFA production from grass biomasses, carbide slag was applied to pretreat four grass biomasses (panicum virgatum, triarrhena lutarioriparia, medicago sativa and turfgrass pruning) before anaerobic fermentation. Results showed that carbide slag pretreatment changed the structure of grass biomasses and enhanced their crystallinity index (CrI). The reducing sugar yield and VFA production from four pretreated grass biomasses were significantly improved. Pretreated turfgrass pruning was the most promising grass compared with other grass biomasses, with the highest VFA production of 8803.87 mg/L and volatile solid removal of 56.76%. Kinetic results indicated that the first order kinetic model, cone model and modified Gompertz model all fitted well with VFA production (R2 > 0.96), and pretreated turfgrass pruning achieved the highest VFA production potential of 11270 mg/L. Turfgrass pruning was the most suitable grass biomass for enzymatic hydrolysis and anaerobic fermentation due to a lower lignin content and CrI. High VS removal revealed that carbide slag pretreatment promoted substrate hydrolysis and further VFA production. There existed a positive correlation between enzymatic hydrolysis and anaerobic fermentation of grass biomasses (R2 = 0.9064). Results of microbial community structure indicated that the high VFA production from turfgrass pruning after carbide slag pretreatment was mainly contributed to enrichment of phylum Firmicutes and genus Clostridium.

In this work, a continuous flow hybrid reactor consisting of visible light photoelectrocatalytic (PE) and reverse osmosis (RO) processes was set for removal of diazinon pesticide from water using nano N-TiO2/Ag/Ti electrode. First, Ag and N-TiO2 nanoparticles were synthesized and simultaneously immobilized on a titanium grid sheet using electrophoretic deposition method to prepare a conductive catalyst. The morphology and composition of the resulting catalyst were characterized by SEM, DRS and XRD. Ability of the prepared catalyst and the effect of operation variables on the degradation of diazinon were investigated using the PE process under the visible light. According to the results, the maximum diazinon degradation efficiency was obtained at pH of 7.5, using 10 wt.% Ag:N-TiO2 ratio, three N-TiO2/Ag/Ti electrodes, visible light intensity of 21 mW cm−2, Na2SO4 as supporting electrolyte and bias potential of 0.9 V. Finally, the N-TiO2/Ag/Ti electrode was used in continuous flow PE/RO reactor for more degradation of diazinon. COD removal results showed 87.5% mineralization of diazinon.

Competition in cooperative coevolution (CC) has demonstrated success in solving global optimization problems. In a recent study, a multi-island competitive cooperative coevolution (MIC3) algorithm was introduced that featured competition and collaboration of several different problem decomposition strategies implemented as independent islands. It was shown that MIC3 converges to high quality solutions without the need to find an optimal decomposition. MIC3 splits the computational budget in terms of the number of function evaluations, equally amongst all the islands and evolves them in a round-robin fashion. This overlooks the difference in contributions of different islands towards improving the overall objective function value. Therefore, a considerable amount of function evaluations is wasted on the low-contributing islands as their problem decomposition strategies may not appeal to the problem at the given stage of the evolutionary process. This paper proposes contribution-based MIC3 algorithms (MIC4) that quantifies the contributions of each island and allocates the computational budget accordingly. The experimental analysis reveals that the proposed method outperforms its counterpart.

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.

Variable interaction is an important aspect of a problem, which reflects its structure, and has implications on the design of efficient optimization algorithms. Although variable interaction has been widely studied in the global optimization community, it has rarely been explored in the multi-objective optimization literature. In this paper, we empirically and analytically study the variable interaction structures of some popular multi-objective benchmark problems. Our study uncovers nontrivial variable interaction structures for the ZDT and DTLZ benchmark problems which were thought to be either separable or non-separable.

Asymmetric price transmission in production institutes can be a reason for the existence of market power. In this regard, by the help of an integrative sample, this study simultaneously analyzes the price transmission between production markets and retails markets of sugar in the presence of the parameter of market power. For this purpose, the seasonal data of the required variables during the years 1995-2013 was used. In order to meet the study objectives, the behavior of the retails price of sugar was evaluated in the framework of two regimes of different changes which were compatible with nature of presenting agricultural products that are widely supplied in the harvest seasons. Also, the inverse elasticity of product supply was used as the parameter of market power. Considering the more probable regime (second regime), it was realized that the marketing elements at the wholesale level intend to price transmission increase more intensively than price decrease to the retail trades level. In this regime, asymmetric price transmission and existence of market power were approved. In the first regime, market power was much more and considering the fact that it has the 30% probability of happening, it has more compatibility with the seasons when supply is abundant. In these seasons, sugar production and refining units increase the market power and create a monopsony market in buying the inputs.