Search

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at . [ABSTRACT FROM AUTHOR]

This paper presents a novel nonlinear control strategy based on a backstepping fuzzy adaptive approach to avoid chaos in a synchronous motor. In the controller design and stability proof, the parameter uncertainty and the system disturbance are taken into account. The matching condition is not satisfied in this model, and disturbances with an unknown upper bound are also exerted on the system. The disturbance and uncertainty are considered in one manifold with an unspecified upper bound. The system stability is verified using the Lyapunov method. The cuckoo optimization algorithm is employed to optimize the adaptive and backstepping control coefficients. [ABSTRACT FROM AUTHOR]

Nulling-type tilt control in tilting railway vehicles, i.e. Single-Input–Single-Output (SISO) control using non-precedent sensor information for lateral acceleration and tilt angle, suffers from performance limitations due to the system's non-minimum phase characteristics [Zolotas, A. C., & Goodall, R. M. (2000).Advanced control strategies for tilting railway vehicles. UKACC international conference on control, University of Cambridge, Cambridge, UK. 6p., ISBN: 0-85296-240-1.]. From an engineering point of view, this is due to the suspension's dynamic interactions and the sensor information used for feedback control. This paper revisits SISO PID-based nulling-type tilt control design (hereby referred to as ‘economical tilt control’) and rigorously studies its design via optimization to improve system performance. The strong coupling between the roll and lateral dynamic modes of the vehicle body is shown and the performance limitations using conventional control highlighted. PID controllers are designed to illustrate different levels of tilt performance regarding the deterministic (curving acceleration response) and stochastic (ride quality) with the latter being a bounded constraint. With novel contribution to use of PID control in the tilt control application with rational transfer functions, particular emphasis is placed on the practical aspects of the tilt dynamics within the design framework via detailed simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a complete solution for constrained control of a permanent magnet synchronous machine. It utilizes field-oriented control with proportional-integral current controllers tuned to obtain a fast transient response and zero steady-state error. To ensure constraint satisfaction in the steady state, a novel field-weakening algorithm which is robust to flux linkage uncertainty is introduced. Field weakening problem is formulated as an optimization problem which is solved online using projected fast gradient method. To ensure constraint satisfaction during current transients, an additional device called current reference governor is added to the existing control loops. The constraint satisfaction is achieved by altering the reference signal. The reference governor is formulated as a simple optimization problem whose objective is to minimize the difference between the true reference and a modified one. The proposed method is implemented on Texas instruments F28343 200 MHzmicrocontroller and experimentally verified on a surface mounted permanent magnet synchronous machine. [ABSTRACT FROM AUTHOR]

In this paper, a study of simultaneous adsorption of mixture dyes of Basic Blue 41 and Basic Yellow 28 in binary system was done using two types of activated carbon and compared with a single dye system in a batch mode. A competitive adsorption between the two cationic dyes was observed and it was noticed that Basic Blue 41 was favored. Kinetics of each dye in single and binary systems was found to follow pseudo-second-order rate kinetic model, with a good correlation (higher than 0.99). The single component equilibrium data were analyzed using the Langmuir and Freundlich isotherms. Overall, the Langmuir isotherm showed a better fitting for all adsorptions under investigation in terms of correlation coefficient. As the binary adsorption is competitive, extended Langmuir models could not predict the binary component isotherm well. The essential parameters which affect the removal efficiency of binary mixture solution such as pH, temperature and adsorbent type were optimized using full factorial design methodology. Effect of parameters and interaction were analyzed using Pareto chart, main effect and interaction effect. In various industrial effluents like textile industries and plant-produced water, dyes are existent in mixture form. So, this work might be of great benefit in knowing to remove the used dyes. [ABSTRACT FROM AUTHOR]

Hole-making is a type of machining processes that are specifically used to cut a hole into a part. The objective of interest in hole-making operations is to reduce the summation of tool airtime and tool switching time in order to reduce total processing time. This objective is affected by the sequence through which operations are done. This problem is formulated as a zero–one nonlinear mathematical programming model. In this paper, a dynamic programming-based method is developed to solve the proposed mathematical model and obtain the globally optimum solutions. An illustrative example is given to show the application and efficiency of the proposed method for optimizing the sequence of hole-making operations in a typical industrial part. The quality of solutions obtained by the proposed method is compared to those obtained by both branch-and-bound method and an ant algorithm available in the literature. The computational experiments reflect the high efficiency of our proposed method. [ABSTRACT FROM PUBLISHER]

This paper develops an optimization model for pricing a monopolistic application software in the presence of piracy. The purpose is raising revenue produced by product’s sale with determining prices in a price skimming strategy and minimizing amount of piracy. The model is a multifunctional price skimming optimization with simplex method which accompanied by a deterministic method for calculating time intervals of each segment. A linear function is used to describe demand of each segment. In addition, a linear piracy function is proposed to make piracy a dynamic parameter. The model has the ability to apply penetration pricing and controlling market share. Rough estimates of Windows 7 sale’s parameters are used to apply in the model. Optimizing case of Windows 7 is resulted in 7.3 percent increase in revenue while value of net market share is virtually constant. Therefore, the developed model demonstrates its competence in optimizing revenue by determining prices with presence of piracy. Results of the research show that to tackle piracy, range of price skimming must be decreased in a way that highest price need to be intensely reduced while lowest one must be slightly reduced. The benefit of using this strategy, is incurring lowest revenue loss due to piracy. The Effects of an escalation in piracy on proposed optimization model include increase in number of sale, demand, selling portion, market share, and decrease in price, price difference between segments, and revenue. [ABSTRACT FROM AUTHOR]

Distributed Generation (DG) integration into an existing electrical distribution system plays a great role in combating power system profile problems associated with load growth, overloading, lowquality of supply and non-reliability. Hence, there is a need to improve the technical benefits of DG integration by optimal sitting and sizing in a power system network. These benefits can potentially defer the investments to be made to upgrade the assets of the distribution system, extend equipment maintenance intervals, reduce electrical power losses, and improve the voltage profile and reliability of the distribution system. The aim of this research is therefore to minimize power losses and improve voltage profile of Bahir Dar distribution network using different types of DGs (type-1 DG, type-2 DG and type-3 DG). An improved particle swarm optimization (IPSO)-based methodology is applied to optimally allocate and size the required DG units. It is found that the proposed algorithm is more effective in enhancing the performance of distributions systems when compared with conventional optimization techniques. In particular, the percentage reduction in real power loss is 55.73%, 73.1% and 54.098% while the percentage reactive power loss reduction is 55.102%, 73.46% and 57.14% for type-1, type-2 and type-3 DGs, respectively. Moreover, the minimum voltage is significantly improved and all bus voltages are maintained above the permissible limit. Generally, the proposed optimization algorithm is found to be more efficient and robust for the performance enhancement of a radial distribution system using multiple DG integrations. [ABSTRACT FROM AUTHOR]

Utilizing exoskeleton devices to help elderly or empower workers is a growing field of research in robotics. The structure of an exoskeleton can vary depending on user's physical dimensions, joints or muscles targeted for assistance, and maximum achievable actuator torque. In this research, a Human-Model-In-the-Loop (HMIL) constrained optimization technique is proposed to design the RoboWalk lower-limb exoskeleton. RoboWalk is an under-actuated non-anthropomorphic assistive robot, that besides applying the desired assistive force, exerts an undesirable disturbing force leading to the user's fall. The HMIL method uses the augmented human-robot 2D model to take RoboWalk and human body's joint torques into account during optimization. The superiority of HMIL method is proven by comparing the results with other strategies in the literature. Obtained results reveal elimination of the disturbing forces, 2 N.m. reduction in average human knee-joint torque, and significant decrease in the actuator required torque. [ABSTRACT FROM AUTHOR]

The optimization of gears is crucial to the development of energy efficient mechanical systems. Weight, volume and power output are major objectives dependent on reduced inertia of rotary, mobile systems and losses in power transmission. In the present work, an extended version of an optimal weight design problem available in literature is investigated using multi-objective teaching and learning-based optimization (MOTLBO). Four design cases differentiated by variable ranges and sets were formulated based on an optimal weight design problem in literature. Power input and contact ratio variables were added to the design problem formulation which was investigated by previous authors as a single objective minimum weight problem. The generated Pareto frontiers were also investigated using decision-making methods viz. Linear Programming for Multidimensional Analysis of Preference (LINMAP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Results obtained reflect the trade-off effects of multiple objectives by increase in optimal weight value as compared to previous studies. The results also highlight the importance of design preference articulation by reflecting on minimum possible results which were better than some obtained in literature. [ABSTRACT FROM AUTHOR]

An adequate and reliable raingauge network is essential for observing rainfall data in hydrology and water resource applications. A raingauge network developed for a catchment area is commonly extended periodically to increase data accuracy. Due to financial constraints, the network is reviewed for the optimal number of stations. A new optimization approach is developed in this study by coupling a cross-validation technique with a geostatistical method for raingauge network optimization to prioritize raingauge stations. The spatial interpolation error of the spatial rainfall distribution, measured as the root mean square error (Erms) optimization criterion is applied to a raingauge network in a tropical urban area. The results indicate that this method can successfully optimize the number of rainfall stations in an existing raingauge network, as the stations are prioritized based on their importance in the network. [ABSTRACT FROM AUTHOR]

In view of the looming energy crisis as a result of depleting fossil fuel resources and environmental concerns from greenhouse gas emissions, the need for sustainable energy sources has secured global attention. Research is currently focused towards renewable sources of energy due to their availability and environmental friendliness. Biofuel production like other bioprocesses is controlled by several process parameters including pH, temperature and substrate concentration; however, the improvement of biofuel production requires a robust process model that accurately relates the effect of input variables to the process output. Artificial neural networks (ANNs) have emerged as a tool for modelling complex, non-linear processes. ANNs are applied in the prediction of various processes; they are useful for virtual experimentations and can potentially enhance bioprocess research and development. In this study, recent findings on the application of ANN for the modelling and optimization of biohydrogen, biogas, biodiesel, microbial fuel cell technology and bioethanol are reviewed. In addition, comparative studies on the modelling efficiency of ANN and other techniques such as the response surface methodology are briefly discussed. The review highlights the efficiency of ANNs as a modelling and optimization tool in biofuel process development. [ABSTRACT FROM AUTHOR]

This study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.Editor Z.W. Kundzewicz; Guest editor S. Weijs [ABSTRACT FROM AUTHOR]

Throughout the literature on design of experiments, the need to perform confirmation runs has been repeatedly emphasized. We provide a high-level framework for design of experiments and show how the confirmation phase is becoming increasingly important. However, the literature provides little guidance on how to assess the confirmation runs nor how many confirmation runs to perform. We provide a review of the literature that addresses confirmation runs. We propose several methods for assessing confirmation runs based on statistical intervals. A simulation study shows which are the best approaches for assessing confirmation runs. We then give some guidance on how many confirmation runs are appropriate and what to do when one fails a confirmation experiment. [ABSTRACT FROM AUTHOR]

Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS) problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven't been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA). PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources. [ABSTRACT FROM AUTHOR]

Optimization algorithms are used in various engineering applications to identify optimal shapes. In this work, we benchmark several unconstrained optimization algorithms (Nelder-Mead, Quasi-Newton, steepest descent) under variation of gradient estimation schemes (adjoint equations, finite differences). Flow fields are computed by solving the Reynolds-Averaged Navier-Stokes equations using the open source computational fluid dynamics code OpenFOAM. Design variables vary from to . The efficiency of the optimization algorithms are benchmarked in terms of: (a) computation time, and (b) applicability and ease of use. Results for lift optimizations are presented for airfoils at a Reynolds number of . As a result, we find for a small number of design variables or less, the computational efficiency of all optimization algorithms to be similar, while the ease of use of the Nelder-Mead algorithm makes it a perfect choice for a low number of design variables. For intermediate and large number of design variables, gradient-based algorithms with gradient estimation through the solution of adjoint equations are unbeaten. [ABSTRACT FROM AUTHOR]

Optimization techniques may be effective in finding alternative design of post-tensioned slabs to improve their mechanical behaviour, particularly reducing the bending moments. Post-Tensioned (PT) concrete one-way slabs are one of the widely used slabs because of their good performance and cost effectiveness compared with other slab types. The objective of this study is to develop three-dimensional Finite Element (FE) model for the optimization of bonded PT concrete one-way slab. The FE software package ANSYS was used to find the optimum strain energy and area of PT tendons. The interface between PT tendon and the surrounding concretes was also modeled, allowing the tendon to keep its shape during slab deformation. Contact algorithm and material nonlinearity were considered in the FE model. The total strain energy of bonded PT concrete slab was considered as the objective function. The design variables are the area of PT tendons, the initial stress in tendons and the eccentricity of the tendon profile while the constraints are the normal stress in concrete, the stress in steel tendon, the shear stress in concrete and the displacement at mid-span of the slab. The optimization results indicate that the area of PT tendons may be reduced by approximately 38% using an appropriate optimization algorithm. We conclude that the optimization of area of PT tendons of the post tensioned slab is very important because, it enable us to find the optimal area of PT tendon with low cost and suitable service conditions [ABSTRACT FROM AUTHOR]