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Artificial Potential Field techniques (APF) have become a popular path planning technique due to their simplicity and fast-execution nature. APF techniques have been widely known for their outstanding performance against the path quality and computational time trade-of problem for path planning techniques. However, Since APF techniques are optimization problems, they suffer from well-known problems such as the minima trap problem and the narrow passage problem. The APF planner can fail to find an obstacle-firee path to the final goal point and simply be trapped in a local minima for some map configurations. This paper proposes a new path planning technique that aims to take the advantage of the APF technique as well as solving the APF problems of local trap and narrow passage problems. The proposed technique is inspired by the fluid dynamic concepts and adopts an artificial "flushing" operation. The robot and the obstacles will be placed in an artificial pool. The robot will have negligible weight while the obstacles will be fixed in the pool. Once the "flushing" operation starts, the robot will be automatically pulled by the water to the flushing point which is the goal point. It is guaranteed that the water in the whole map will be absorbed into the flushing point and no particles will be trapped in any region. The simulation results have proven the effectiveness of the proposed technique and demonstrated its ability to overcome the challenge of local minima traps in potential field path planning. [ABSTRACT FROM AUTHOR]

The supply chain regards suppliers, producers, and consumers as an organic whole, unifying and coordinating the information flow, logistics, and capital flow of all members, and achieving the goal of win-win for all members in the overall operation of cross organization. Demand forecasting is an important factor driving the entire supply chain, and low error rates in forecasting are a common goal pursued by the industry. In order to improve the quality of demand forecasting, enhance the efficiency of supply chain operations, and leverage the important role of machine learning in the era of artificial intelligence, this paper conducts research based on LSTM. Firstly, this paper determines the objective function and constraints for supply chain demand forecasting; Then, this paper constructs a supply chain demand prediction model, based on the LSTM network structure, determine the network training method and model construction process; Finally, this paper conducts simulation experiments and result analysis, configure LSTM parameters, determine model performance evaluation indicators, and compare and analyze actual values with predicted values. The results indicate that the supply chain demand prediction model constructed in this article has very good performance and has promotional value in practice. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks always operate within the context of diverse and constrained characteristics of the devices. Low-Power and Lossy Networks (LLNs) constitute a network architecture commonly utilized in IoT application deployments, facilitating networking and the establishment of paths for data transmission. The Routing Protocol for Low-Power and Lossy Networks (RPL) demonstrates promising capabilities for LLN network operations, supporting IPv4 and IPv6-enabled services. The RPL protocol constructs a Destination Oriented Directed Acyclic Graph (DODAG) logical routing topology based on defined Objective Function (OF) metrics. Routing operations within the DODAG utilize these metrics and constraints to select parent nodes and calculate optimal routes between two nodes. Standardized OFs have traditionally focused on either parent node selection or routing objectives within the DODAG, often treating load balancing and bottleneck optimization separately. However, their combined impact on RPL's effectiveness has been overlooked. This paper introduces an Adaptively Composite Objective Function (AC-OF) approach that considers the combined objectives of DODAG load balancing and optimized routing operations. Through simulation evidence, the paper presents improved network parameters. The AC-OF implementation brings out significant results in the form of a balanced DODAG topology and it has good impacts on data transmission, control overhead messages, parent switching, delay, energy consumption, and node lifetime. [ABSTRACT FROM AUTHOR]

Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are everchanging in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques. [ABSTRACT FROM AUTHOR]

The machinery manufacturing industry has always been an important pillar of China's economic development. When we solve different problems in mechanical production, people often face the trade-off of having to choose among some desired multiple optimization goals, because many goals cannot often be optimized at the same time, or even conflict with each other. To improve the comprehensive performance of hydrodynamic sliding bearing, the improved multi-objective model is applied to this problem to obtain more reasonable parameter design. We use MOPSO with linear weighting method to solve our proposed model. The results of a numerical example show that our model and algorithm are feasible and more effective than the traditional MOPSO by more reasonable parameter design. [ABSTRACT FROM AUTHOR]

• The core findings from our study are: • Presents new prior knowledge based method to determine the optimal split arc in VMAT. • Fast and effective approach to improve the Plan quality for VMAT. • The proposed split arc VMAT strategy is shown to reduce the dose to the OARs. • Low dose volume, beam on time and monitor units are reduced by proposed split arc strategy. We present a new approach to determine the optimal arc split for VMAT beams which is an extension of our recently published algorithm for selecting optimal beam angles in Intensity Modulated Radiation Therapy (IMRT) The proposed approach uses an objective function based scoring method called " ψ – score" to determine optimal arc splitting strategy. To validate our approach, we applied it in different clinical cases: Abdomen-Para aortic node, Lung, Pancreas and Prostate. Basically, for all clinical cases, two set of plans were created, namely VMAT plan and VMAT_S plan using Pinnacle3 (V16.2, Philips Medical Systems (Cleveland), Inc.). In the VMAT plans, full arc (360°) with 4-degree gantry spacing was used during optimization to compute the " ψ – score". Subsequently the avoidable arc portions were identified and removed using the ψ – score plot followed by the final optimization (VMAT_S). Equivalent or better OAR sparing, and similar target coverage were achieved in VMAT_S plans compared to VMAT plans. VMAT_S reduced the number of control points and monitor units by 24.2% and 12.9% respectively. On the average, beam on time was reduced by 21.9% and low dose volume (5 Gy isodose volume) to healthy tissues was reduced by 4.9% in VMAT_S compared to VMAT plans. The results demonstrated that the proposed method is useful for reducing the monitor units, beam on time and low dose volume without significantly compromising plan quality and most useful for non-centrically located targets. [ABSTRACT FROM AUTHOR]

Resource scheduling (RS) for makespan optimization in a cloud computing (CC) environment is an important aspect of handling effective resources in the cloud. Makespan optimization defines the minimization of time required to complete a collection of tasks in a computational environment. In the context of CC, makespan optimization aims to reduce the overall time required to execute tasks while effectively allocating and managing resources. RS in CC is a difficult task because of the number and variation of resources accessible and the volatility of usage-patterns of the resource assuming that the resource setting is on the service providers. Therefore, this article presents a Farmland Fertility Algorithm based Resource Scheduling for Makespan Optimization (FFARS-MSO) in Cloud Computing Environment. The presented FFARS-MSO technique is mainly based on FFA, which is stimulated by the farmland fertility in nature where the farmers split the various regions of the farm based on soil quality, and thereby every region's soil quality is distinct from others. In addition, the presented FFARS-MSO technique is utilized for load balancing and uniform distribution of resources depending upon the demand. The simulation outcomes ensure that the FFARS-MSO approach has reached effectual resource allocation over other optimization algorithms. [ABSTRACT FROM AUTHOR]

Endurance Time (ET) method is a dynamic analysis procedure in which increasing excitations are imposed on structures; these excitations are known as Endurance Time Excitation Functions (ETEF). This study presents a method to determine an optimal objective function for simulating ETEFs including unconstrained optimization problems. In optimization problems, equations are defined in terms of an objective function. In the problem of simulating ETEFs, the objective function can be defined in many different ways regarding the considered intensity measures and respective weighting factors. In addition, the type of calculating residuals (absolute way or relative way) diversifies the objective function definitions. The proposed method for determining the optimal objective function includes quantifying the accuracy of ETEFs in a scalar quantity regardless of their objective functions and introducing an approach to attenuate the dependence of results on the initial points of optimizations. The proposed method is applied, and results are then presented. It is observed that considering only acceleration spectra and calculating residuals in a relative way lead to more accurate ETEFs. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The process of electrical discharge profiling of diamond tool is regarded as a stochastic control object from the point of view of the analysis of factors affecting its productivity. The selection of optimal modes can significantly improve the quality indicators of this process. For this improvement, a model of electrical discharge profiling is developed using the example of diamond grinding wheels, which allows making an informed selection of optimal control modes for this process. The main factor that reduces productivity is the insufficient average power released in the interelectrode gap during electrical discharge machining of the workpiece. The dependence of the indicated power on the size of the interelectrode gap and on the frequency and amplitude of technological pulses is extreme, which determines the choice of approach to optimizing the process. To ensure extreme power control, it is proposed to use the Gauss-Seidel coordinate descent method for two parameters: the size of the interelectrode gap and the amplitude of the pulse generator. The search algorithm for the extremum of the static characteristics of the inertial object for each coordinate is performed with the help of the recursive least squares method. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ambiente e Água is the property of Revista Ambiente e Agua and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the optimal control of a class of non-linear value-added HIV model with control term is studied by using Pontryagin minimum principle under the condition of stability of equilibrium point established by stability theory. By introducing drug control items and setting objective functions, the optimal solution can be obtained, and finally the sum of infected cells and medical costs can be minimized. [ABSTRACT FROM AUTHOR]

• We propose a new metric for ranking the beam angles in IMRT planning in the order of their optimality. • Employing the beam angles selected using the proposed approach has considerably improved the IMRT plan quality. • The computation time to get optimal beam angles is in the range of 15–25 min. We propose a novel method for the selection of optimal beam angles in Intensity Modulated Radiation Therapy (IMRT). The proposed approach uses an objective function based metric called "target-to-critical organ objective function ratio" to find out the optimal gantry angles. The beams are ranked based on this metric and are accordingly chosen for IMRT optimization. We have used the Pinnacle TPS (Philips Medical System V 16.2) for performing the IMRT optimization. In order to validate our approach, we have applied it in four clinical cases: Head and Neck, Lung, Abdomen and Prostate. Basically, for all clinical cases, two set of plans were created with same clinical objectives, namely Equal angle plan (EA Plan) and Suitable angle Plan (SA Plan). In the EA plans, the beam angles were placed in an equiangular manner starting from the gantry angle of 0°. In the corresponding SA plans, the beam angles were decided using the guidance provided by the algorithm. The reduction in OAR mean dose and max dose obtained in SA plans is about 3 to 16% and 3 to 15% respectively depending upon the treatment site while obtaining equal target coverage as compared to their EA counterparts. It takes approximately 15–25 min to find the optimal beam angles. The results obtained from the clinical cases indicate that the plan quality is considerably improved when the beam angles are optimized using the proposed method. [ABSTRACT FROM AUTHOR]

The bi-partial criterion for clustering problem consists of two parts, where the first one takes into account intracluster relations, and the second -- inter-cluster ones. In the case of k-means algorithm, such bi-partial criterion combines intra-cluster dispersion with inter-cluster similarity, to be jointly minimized. The first part only of such objective function provides the "standard" quality of clustering based on distances between objects (the wellknown classical k-means). To improve the clustering quality based on the bi-partial objective function, we develop the permutable version of k-means algorithm. This paper shows that the permutable k-means appears to be a new type of a clustering procedure. [ABSTRACT FROM AUTHOR]

The prediction accuracy of chemical kinetics models can be improved efficiently by using automatic model optimization techniques. In the optimization, an objective function, which quantifies the differences between model responses and experimental data for quantities of interest, is minimized by calibrating the reaction rate parameters of a model within their uncertainty limits. Consequently, the values of the model predictions become closer to those of the measurements. Typically, a point-wise objective function, which is based on function components separately for each measurement over the investigated domain, is used in the model optimization. Quantities of interest are often functions of various physical condition parameters, such as temperature, pressure, and equivalence ratio. However, the point-wise objective function does not consider the correlation between data and their corresponding physical conditions. Thus, in this work, a new objective function is proposed, which uses a surface-matching (SM) method. It evaluates the similarity between surface shapes of the predicted and measured values, which is quantified in form of two user-defined physical condition parameters. By minimizing this function, the joint dependence of model predictions on physical conditions is optimized in conjunction with the point-wise model prediction accuracy. A chemical mechanism of oxymethylene ethers is optimized in this work as an example. The model is calibrated with the point-wise, curve-matching (CM)-based, and SM-based objective functions. The optimized models are compared and the results are discussed. It is shown that the optimization with the SM-based objective function yields improved results for certain cases compared to using the point-wise objective function. This model also provides the best prediction accuracy in terms of joint physical condition dependence. In addition, a better overall performance is achieved by adjusting the ratios between the component functions in the objective function, which demonstrates that the definition of objective functions plays a crucial role for model optimization. [ABSTRACT FROM AUTHOR]

In this paper, a graphical solution of the nonlinear programming problem was presented the result satisfies the graphical solution of the nonlinear programming problem and we also solved some NLPP question in this paper by graphical method. [ABSTRACT FROM AUTHOR]

At present, China has problems such as irrational energy structure and low energy utilization efficiency. Therefore, the optimization of energy structure is an inevitable trend of social and economic development. Based on the Analytic Hierarchy Process (AHP), the optimization methods for renewable energy structure of low-carbon ecosystems have been studied in depth through four criteria indicators: economy, technology, resources and environment. At the same time, the single-index quantification-multi-indicator integration evaluation method is used to comprehensively evaluate six renewable energy sources. According to the comprehensive index obtained, the renewable energy development priorities are reordered. The multiobjective planning model for optimizing the renewable energy structure in low-carbon ecosystems is constructed by maximizing the combination of six renewable energy comprehensive evaluation scores. The decision variables of the model are coal, oil, natural gas and six kinds of renewable energy. The costs of CO2 emission and pollution control are minimized as the objective function of the model to maximize the overall benefits of the renewable energy portfolio. The experimental results show that the optimal ratio of hydropower, wind energy, solar energy, biomass energy, geothermal energy, and ocean energy is 38.5%, 35.7%, 16.8%, 3.0%, 3.0%, and 3.0%, respectively. The proposed optimization method shows that the proportion of optimal energy consumption predicted at the end of 2020 is 40.12% for coal, 21.42% for oil, 8.66% for natural gas, and 28.35% for renewable resources. This shows that coal is still the main source of energy in the future low-carbon ecosystem. The proportion of renewable energy represented by wind power, nuclear power, and hydropower in the overall energy structure has rapidly increased, gradually replacing traditional energy sources. [ABSTRACT FROM AUTHOR]

This article describes a new clustering method for segmentation of Magnetic resonance imaging (MRI) brain images. Currently, when fuzzy clustering is applied to brain image segmentation, there are two main problems to be solved which are: (i) the sensitivity to noise and intensity non-uniformity (INU) artifact; (ii) the trapping into local minima and dependency on initial clustering centroids. For the purpose of obtaining satisfactory segmentation performance and dealing with the problems mentioned above, an effective method is developed within the scope of this paper. Firstly, a new objective function utilizing kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) is designed. We then propose a new algorithm based on an improved particle swarm optimization (PSO) with the new fitness function to better segment MRI brain images. To test its performance, the proposed algorithm has been evaluated on several benchmark images including the simulated MRI brain images from the McConnell Brain Imaging Center (BrainWeb) and the real MRI brain images from the Internet Brain Segmentation Repository (IBSR). In addition, a systematic comparison of the proposed algorithm versus five other state of the art techniques is presented. Experimental results show that the proposed algorithm can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provide better results than its competitors. [ABSTRACT FROM AUTHOR]

Building energy retrofitting has emerged as a primary strategy for reducing energy use and carbon emissions. The decision about which retrofit measures should be implemented in a particular project is a single- or multi-objective optimization problem subject to many constraints and limitations. Different objective(s) have been used in the literature, however the identification and selection of the objective(s) for this optimization problem is still a challenge. This study develops a decision matrix that guides decision-makers on how to select the objective(s) for a single- or multi-objective optimization problem that results in the selection of the best energy retrofitting strategy, by considering and defining the “investor benefits” term, the type of potential investors, and the type of potential benefits of building energy retrofits. Four types of investors are considered in this study: owner-occupant, absent owner, leaser, and external investor. In addition, a case study is used to illustrate how different types of investors may affect the selection of objective functions and, therefore, the final decisions for optimum energy retrofits. Results show that when the investor is the owner-occupant, a higher investment is suggested for energy retrofits to achieve optimum benefits. [ABSTRACT FROM AUTHOR]

The opportunistic spectrum access (OSA) has emerged as promising approach to increase the efficiency in spectrum sharing. The important feature of the OSA is to enable the unlicensed users or secondary users (SUs) to search for unused spectrum vacated by idle licensed user or primary user (PU). In this paper, we consider the maximization of the number of frames sent by the SUs during the idle period (white space) of the PU, while avoiding the infringement into the privileges of the PU. We set up this problem in terms of an optimal stopping problem. Explicit expression for the optimal number of frames that can be sent by the SUs in a white space is derived. Performance analysis for channel utilization and channel idle time is presented. Finally, the analytical results for optimal number of frames sent by SU in white space are numerically illustrated. [ABSTRACT FROM AUTHOR]

The solution of the problem of optimization of the university organization is proposed on the base of using a fuzzy model of nonlinear mathematical programming. This fuzzy approach allows to aggregate the expert estimates of influence obtained at the initial stage and, thereby, to more accurately evaluate the measures of effectiveness of the university organization. [ABSTRACT FROM AUTHOR]

The Multilayer Perceptron (MLP) is the most useful artificial neural network to estimate the functional structure in the non-linear systems, but the determination of its architecture and weights is a fundamental problem due to their direct impact on the network convergence and performance. This paper presents an extension of our latest work where we have treated the problem of optimizing the number of connections weights and hidden layers in MLP. We introduce an objective function regularized by the sum of connections used by the network and weight decay, to solve the resulting model, we use a genetic algorithm and we train the network with back-propagation method. To test our methodology we use a data classification: wine, iris, seeds and medical data (Cancer, thyroid). We compare the obtained results with those of similar works existing in the literature. The numerical results illustrate the advantages of our approach as a new method of architecture optimization and training. [ABSTRACT FROM AUTHOR]

In both wired and wireless networks, data routing along the best path is constantly a top priority for the research community. Currently, the most widely adopted routing algorithms in the Internet of Things is the Routing Protocol for Low Power and Lossy Networks (RPL). In RPL, selecting the optimal path primarily depends on the Objective Function (OF). Multi-attribute OFs are thought to be a viable method for choosing the best path, but the main concern here is how these attributes must be coupled. Such issues are being taken care by fuzzy based technologies as it deals with imprecise or distorted attribute values. Most of the fuzzy logic-based OFs are mainly based on the Mamdani method, which has low accuracy. However, a better accuracy is observed in Takagi–Sugeno method. Hence the paper develops a Fuzzy-based Takagi and Sugeno Objective Function (FTSOF) for RPL. The FTSOF technique combines the routing metrics to give a crisp value based on which the parent node is selected. The proposed approach is implemented in the Contiki operating system. The simulation was carried out on different network densities and various data rates. The performance parameters considered are packet delivery ratio, latency, network setup time, and control message overhead. The results shows that FTSOF outperforms the state-of-the-art techniques such as OF-0, MRHOF, and the Fuzzy-based Mamdani Objective Function (FMOF). [ABSTRACT FROM AUTHOR]

Animal nutrition is a very large area of study where mathematical modeling is a success, as in many areas. In addition to shaping the main physiological processes underlying the farm animals yielding, the objective function optimization issue (i.e. total expenditure on fodder) arises both in terms of protein and vitamin's intake of nutrients and in terms of their cost. Optimising feed rations through mathematical modeling monitors the amount of food an animal needs throughout the day, so that the objective function (also called goal function of efficiency function) be minimum or maximum depending on the problem requirements. In order to solve mathematical model one needs fodder varieties that are part of the fodder basis and this feed (in kg) must be purchased or produced, and the solving method we use is the Solver tool of Microsoft Office Excel software. In order to optimize the objective function one should observe the following restrictions concerning the provisioning of total feed expressed in nutrients: provisioningof the necessary fodder by groups of fodder, observing proportions in fodder structure by groups of fodder, complying with fodderplants culture technologies and taking into account the availability of land designated for the forage and fodder crops. The mathematical model consists of three parts: the system of restrictions, non-negativity conditions and objective function. This paper shows the effectiveness of the Solver IT tools in dealing with many linear programming issues, including the establishment of feed rations as ensuring a good and well balanced food in terms of nutrition ensures both a good maintenance of the animals and an increased production. [ABSTRACT FROM AUTHOR]

An optimal power control algorithm based on convex optimization is proposed for base stations in long term evolution networks. An objective function was formulated to maximize the proportional fairness of the networks. The optimal value of the objective function was obtained using convex optimization and distributed methods based on the path loss model between the base station and users. Field tests on live networks were conducted to evaluate the performance of the proposed algorithm. The experimental results verified that, in a multi-cell multi-user scenario, the proposed algorithm increases system throughputs, proportional fairness, and energy efficiency by 9, 1.31 and 20.2 %, respectively, compared to the conventional fixed power allocation method. [ABSTRACT FROM AUTHOR]

To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision method is proposed in this paper to adapt to the fast channel variations. Firstly, the reduced decision rules are obtained according to the rough set (RS) theory. Secondly, the randomly generated initial population of the genetic algorithm (GA) is screened and the individuals are preserved in accordance with the reduced decision rules. Finally, the initial population after screening is utilized in the genetic algorithm to optimize the communication parameters. In order to remove the dependency on the weights, this paper deploys an anti-jamming decision objective function, which aims at maximizing the normalized transmission rate under the constraints of minimizing the normalized transmitting power with the pre-defined bit error rate (BER). Simulations are carried out to verify the performance of both the traditional genetic algorithm and the adaptive genetic algorithm. Simulation results show that the convergence rates of the two algorithms increase significantly thanks to the initial population determined by the reduced-rules, without losing the accuracy of the decision-making. Meanwhile, the weight-independent objective function makes the algorithm more practical than the traditional methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article deals with modeling of astronomical objects, which is one of the most fundamental topics in astronomical science. Introduction part is focused on problem description and used methods. Point Spread Function Modeling part deals with description of basic models used in astronomical photometry and further on introduction of more sophisticated models such as combinations of interference, turbulence, focusing, etc. This paper also contains a way of objective function definition based on the knowledge of Poisson distributed noise, which is included in astronomical data. The proposed methods are further applied to real astronomical data. [ABSTRACT FROM AUTHOR]

The multi-objective energy consumption scheduling problem based on the third-party management is one essential issue of smart grid. The minimal energy cost and the maximal utility are two optimization objectives. One characteristic of the multi-objective energy consumption scheduling problem is that the magnitude difference between the two objectives increases as the number of users increases. The difference affects the quality of the solution set in the sense that it is harder to obtain the uniformly distributed solutions with good convergence and diversity for problems with a larger difference. In this paper, we propose a decomposition algorithm based on the non-uniform weight vector. The weight vector is designed based on the relationship of the magnitude difference between the two optimization objectives. The weight vector consists of two parts. One is the balance factor that can maintain the diversity of the solutions. The other is the scale factor that further enhances the searching ability with a large number of users. With the non-uniform weight vector, our algorithm can effectively deal with the magnitude difference between the two optimization objectives. The simulation illustrates that the proposed algorithm is very useful for solving large-scale energy consumption scheduling problems. In addition, the modified standard ZDT problems with apparent magnitude differences between two objectives are used to illustrate the versatility and stability of the proposed algorithms based on the non-uniform weight vector. [ABSTRACT FROM AUTHOR]

In this paper, in situ field measurements and analytical model verification studies of a railway bridge that spans Karakaya Dam Lake between the cities of Malatya and Elazig in Turkey are presented. The bridge consists of 29 simply-supported steel trusses, each having a 64 m span length, giving a total bridge length of 2029.60 m. To understand the actual behaviour of the bridge, static and dynamic tests on the bridge spans were performed. The bridge was instrumented with strain-gauges mounted on the surface of the bridge members, accelerometers placed on the piers, abutments, and truss members and an optical laser based displacement sensor located at mid-span. Then, a three-dimensional finite element model of the bridge was generated using commercially available finite element analysis software using frame elements, a thick shell, and spring and rigid bar elements. The initial numerical model was calibrated using an optimization procedure based on automatically adjusting the spring constants and rigid bar rigidities with the help of MATLAB. Finally, the validity of the calibrated FE model was examined by the vertical displacement measurements. [ABSTRACT FROM AUTHOR]

In this study, six E. coli strains, which harboring enhanced green fluorescence protein (EGFP), an intein (INT) and ice nucleation protein (INP), were used to express the EGFP protein. The effect of E. coli strains on the surfaced displayed EGFP via INP-INT system was first investigated. Among these strains, E. coli JM109(DE3) was found the best strain for EGFP production (67.9 mg/L), two-fold as compared to that produced by E. coli DH1(DE3). It was concluded that lon mutation could stabilize recombinant proteins. Thus, the deletion of the gene coding for proteolysis might be a critical factor when using host for surface-expression production. The optimal conditions for cell cultivation, gene induction and intein cleavage for EGFP production were systematically studied and discussed. The objective function (O) value of 26.5 can be reached at a 12-h induction and 20-h cleavage process, where the EGFP yield is 58.6 mg/L with a purity of 45.3%. Furthermore, results indicated that the higher intein activity was obtained in the shorter cleavage duration; however, the shorter cleavage time sacrificed the production yield as expected. To compromise between the production yield and purity, the optimal conditions for the EGFP production were: 12-h induction and 20-h cleavage process is for the production of higher purity EGFP. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. [ABSTRACT FROM AUTHOR]

As a new paradigm in the field of evolutionary computation, multifactorial evolution has become more and more popular since its inception. It attempts to solve multiple optimization problems simultaneously using a single evolving population. Due to the implicit knowledge transfer, multifactorial evolution exhibits the potential to solve complex optimization problems. This paper tries to take advantage of multifactorial evolution to solve constrained optimization problems (COPs). To this end, we derive two different optimization problems from the considered COP. Theoretical analysis reveals that the optima of these two problems are exactly identical to the feasible optima of the original COP. Thus, the advantages of knowledge transfer can be used adequately. In addition, these two problems focus more on the objective function and the constraints, respectively. By solving them concurrently, we can achieve the balance between constraints and objective function, which is of essential importance in constrained evolutionary optimization. Moreover, a multifactorial differential evolution is developed, which can leverage the merits of multifactorial evolution and differential evolution effectively. To tackle complex COPs, a diversity strategy is designed for population diversity maintenance. Extensive experiments on benchmark test sets and engineering optimization problems have demonstrated the effectiveness of the proposed method. • We make the first attempt to leverage the idea of multifactorial evolution for constrained optimization. • To effectively utilize the advantages of multifactorial evolution, two optimization problems, whose optima are identical to that of the original COP, are derived. In addition, a novel multifactorial DE algorithm is proposed to solve these two problems concurrently. • A diversity strategy is designed to tackle complicated COPs. [ABSTRACT FROM AUTHOR]

The pickup and delivery problem with time windows (PDPTW) is an important problem to be solved. This is due to its resemblance to real-life services such as food delivery, courier service, curbside pickup service, and dial-a-ride, among others. However, the research on multi-objective pickup and delivery problems with time windows (MOPDPTW) remains scarce even after many years. The MOPDPTW objective is to achieve the optimal solution on both the number of vehicle and total travelled distance. It is challenging to obtain optimal results, the recurring optimal results that has least difference in magnitude and broader Pareto sets at the same time. Hence, an algorithm that can fulfil these requirements are highly sought after. This paper presents a mediocre evolutionary distributed microservices re-optimization algorithm (MEDMRA) to solve the MOPDPTW. The combination of the re-optimize algorithm and mediocre evolutionary algorithm escape the local optimal, optimized results and produce broader Pareto value. We compare our results using Li & Lim instances. Our results outperform the latest published hybrid algorithms and competitive with the best-known solutions. • MEDMRA consists of an evolutionary cycle and re-optimization cycle. • Mediocre solutions during solution evaluating and extraction process. • Harness exploration and exploitation strength which escaping the local optimal. • MEDMRA outperforms the published hybrid algorithm. • MEDMRA has a wider Pareto optimal front and the least difference in magnitude. [ABSTRACT FROM AUTHOR]

Intelligent mechatronic systems, such as self-optimizing systems, allow an adaptation of the system behavior at runtime based on the current situation. To do so, they generally select among several pre-defined working points. A common method to determine working points for a mechatronic system is to use model-based multiobjective optimization. It allows finding compromises among conflicting objectives, called objective functions, by adapting parameters. To evaluate the system behavior for different parameter sets, a model of the system behavior is included in the objective functions and is evaluated during each function call. Intelligent mechatronic systems also have the ability to adapt their behavior based on their current reliability, thus increasing their availability, or on changed safety requirements; all of which are summed up by the common term dependability. To allow this adaptation, dependability can be considered in multiobjective optimization by including dependability-related objective functions. However, whereas performance-related objective functions are easily found, formulation of dependability-related objective functions is highly system-specific and not intuitive, making it complex and error-prone. Since each mechatronic system is different, individual failure modes have to be taken into account, which need to be found using common methods such as Failure-Modes and Effects Analysis or Fault Tree Analysis. Using component degradation models, which again are specific to the system at hand, the main loading factors can be determined. By including these in the model of the system behavior, the relation between working point and dependability can be formulated as an objective function. In our work, this approach is presented in more detail. It is exemplified using an actively actuated single plate dry clutch system. Results show that this approach is suitable for formulating dependability-related objective functions and that these can be used to extend system lifetime by adapting system behavior. [ABSTRACT FROM AUTHOR]

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management). [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (OA) is one of the most common diseases among the elderly people. Typically, medical attention is not sought until the disease has progressed to a point at which it is not possible to treat effectively, often due to concerns over the cost of detection at an earlier stage. Ultrasound (US) imaging has a number of advantages as an imaging technique; apart from being a low cost diagnostic method, it is also non-invasive, utilizes non-ionizing radiation and portable. Due to progression of knee OA, the cartilage will experience a significant change in shape, and it becomes degenerated. After image processing using US medical imaging, it is possible to detect the cartilage shape change of the knee joint. Low contrast ratio and speckle noise are two main disadvantages of US imaging. The aim of this paper is to present a method for enhancing the contrast of the US image of knee joint cartilage in detecting early stages of knee OA. Conventional contrast enhancing methods are known to have some limitations. The objective of this paper is to propose a new contrast enhancing method which can overcome the limitations of the conventional contrast enhancing method. Most conventional contrasts enhancing methods emphasize only on one character. In the proposed method, the optimum value of contrast, brightness and detail preservation are considered. The proposed method is applied to find out the optimum separating point for segmenting the histogram of US image, for which optimum value of contrast, brightness and detail preservation will be preserved. In this method as well, three metrics, named as Preservation of Brightness Score function (PBS), Optimum Contrast Score function (OCS) and Preservation of Detail Score function (PDS), are defined. [ABSTRACT FROM AUTHOR]

Flowsheet of industrial refining straight-run gasoline on zeolite catalyst includes the necessary stage of fractionation of conversion products to produce commercial gasoline, gas and heavy residue. Changes in qualitative and quantitative compositions of the catalytic conversion products under catalyst deactivation require current parametrical optimization of this stage. Objective functions that take into account catalyst deactivation and the constrains depending on the requirements for product quality and equipment specifications were developed. Optimal conditions were found to differ significantly from those designed for fresh catalyst. [ABSTRACT FROM AUTHOR]

The task of multi-objective constrained optimization of chrome tanning of semi-finished leather applying the Hooke-Jeeves method is solved. The optimality criterion is formulated in the form of a generalized additive objective function. Local criteria for the objective function are normalized and nondimensionalized. The weighting parameters are obtained on the basis of professional assessments. The algorithm of the modified Hooke-Jeeves method is the basis for the software module, which is implemented using object-oriented programming language Visual Basic for Application. The possibility to work with explicit and implicit constraints in the software module is provided. The developed software module is used to find the optimal values of the generalized objective function. For a low-waste technology of chrome tanning of semi-finished leather with dry chrome tanning agent its mathematical description is obtained and its constraints on the technological parameters of the process are determined. The optimization problem of the dry chrome tanning of semi-finished leather process is solved and the optimal values of the parameters of the tanning process are determined. [ABSTRACT FROM AUTHOR]

Abstract: There has been increasing attention to optimal design and operation of natural gas liquefaction processes. This paper examines the operation optimization of propane precooled mixed refrigerant (C3MR) and C3MR with split propane (C3MR-SP) processes. The optimization problem is formulated using four objective functions including shaft work consumption, two different exergy efficiency expressions, and operating expenditure (OPEX) to identify process performance improvements. The best performing objective function for C3MR process is found to be exergy efficiency (considering power and cooling duty) followed by OPEX. For C3MR-SP, however, shaft work is found to be the best objective function, OPEX being the second best performer. The findings indicate that for performance optimization of an installed LNG train, achieving the lowest specific shaft work together with the highest exergy efficiency is not possible for fixed feed natural gas flowrate and a fixed value of heat transfer coefficient and exchanger area, UA. The results of this work are then compared with numerous available literature studies; comparisons of optimal shaft work or optimal exergy efficiency across literature studies are impractical because of the dissimilar process conditions used, such as natural gas composition and pressure, pressure of LNG product, and UA of main cryogenic heat exchanger (MCHE). [Copyright &y& Elsevier]

Abstract: Simulation models are widely used for studying physical processes such as surface runoff, sediment transport and sediment yield in catchments. Most models need case-specific empirical data for parameterization before being applied especially in regions other than the ones they have been developed. Sensitivity analysis is usually performed to determine the most influential factors of a model so that they can be prioritized for optimization. In this way uncertainties in model outputs can be reduced considerably. This study evaluates the commonly used modified universal soil loss equation (MUSLE) model used for sediment yield simulation for the case of the upper Malewa catchment in Kenya. The conceptual factors of the model are assessed relative to the hydrological factors in the model. Also, the sensitivity of the model to the choice of the objective function in calibration is tested. The Sobol' sensitivity analysis method was used for evaluating the degree of sensitivity of the conceptual and hydrological factors for sediment yield simulations using the MUSLE model. Nash-Sutcliffe Efficiency (NSE) and the modified Nash-Sutcliffe Efficiency (NSEm) are used to test the sensitivity of the model to the choice of the objective function and robustness of model performance with sediment data measured from upper Malewa catchment, Kenya. The results indicate that the conceptual factors are the most sensitive factors of the MUSLE model contributing about 66% of the variability in the output sediment yield. Increased variability of sediment yield output was also observed. This was attributed to interactions of input factors. For the upper Malewa catchment calibration of the MUSLE model indicates that the use of NSEm as an objective function provides stable results, which indicates that the model can satisfactorily be applied for sediment yield simulations. [Copyright &y& Elsevier]

Abstract: Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths. [Copyright &y& Elsevier]

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Efficiency is an everlasting issue which is of special importance to any economic society. In the modern market economy society, inefficiency means losing competitiveness. It is imperative for cooperatives to improve economic efficiency, thus maintaining their competitiveness. Generally there are two aspects in the concept of cooperative efficiency. On the one hand, it can be understood as cooperative input-output efficiency, which means to obtain maximum output at a certain level of input, or to realize minimum input at a certain level of output. This belongs to internal efficiency for cooperatives. On the other hand, it also can be regarded as external efficiency for cooperatives, which means the comprehensive efficiency of cooperatives in promoting reasonable allocation of social resources among various departments while pursuing internal efficiency. In short, external efficiency refers to cooperatives' contribution to total welfare in society. However, the external efficiency for cooperatives, in other words, the ideal Pareto optimality, does not exist in reality and it would be difficult to carry on quantitative evaluation. Therefore, it is realistically reasonable for cooperatives to pursue input-output efficiency. The cooperative efficiency is determined by the chosen objective function. Cooperatives with different functions have different objective functions. For example, objective functions for marketing cooperatives and for purchasing cooperatives are different. Identical objective function is the basic premise for measuring cooperative efficiency. It is an accidental phenomenon for cooperatives to realize efficiency because of information asymmetry, poor management etc. Generally inefficiency manifests in cooperatives. Therefore, there is practical significance of adopting effective methods to measure cooperative efficiency accurately and in discussing profound reasons affecting the efficiency. The influence of scale on cooperative efficiency is obvious. It is generally believed that there exists an optimal scale for each cooperative, with which the cooperative can reach its maximum efficiency. It also should be noted that cooperatives need an appropriate scale, which may grow bigger with the expanding of social-economic scale. Only the cooperative has dynamic adaptability in terms of scale can it achieve higher efficiency in increasingly fierce global competition. Internal control factors have essential impact on cooperative efficiency. Among these factors, staff structure and manager's human capital are the most important ones. To some extent, the process of improving cooperative efficiency is the process of constantly improving staff and manager's quality and ability. It is also clear that a certain size of board members and the supervision of certain members from supervisory board can improve cooperative efficiency. The external environments such as institution, resource and market also affect resource allocation and utilization in cooperatives though influencing internal control factors, and then affect cooperative efficiency. Therefore, a cooperative which can achieve input-output efficiency has to rely on the coordinative operation between internal control mechanism and external environments. As far as policy is concerning, investment on staff, including managers, is the key to improve cooperative efficiency. Given the fact that most staff members in Chinese cooperatives have weak cooperative awareness and are incompetent in management, investing on them is especially significant. In particular, the approaches to promote efficiency of cooperatives include improving staff's human capital by education and training and improving their cooperative awareness, discussion competence, managerial capability, and response to environment. In addition, it is necessary to consider the role of external environments. Besides continuing to perfect relevant laws and regulations and creating a better institutional environment, other factors such as regional resources environment should also be taken into consideration. It is important to adjust policies to accord with different resource factors in different regions in order to produce a positive incentive effect on the development of cooperatives. [ABSTRACT FROM AUTHOR]

Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like Kp, Ki, Kd, and α with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems. [ABSTRACT FROM AUTHOR]