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This paper presents the comparison ofselective harmonic elimination in a cascaded h-bridge multilevel (CHBMLI) inverter using PSO and TLBO optimization algorithms. The inverters are employed in a variety of applications, including high-voltage dc transmission, variable drive systems, and electrical vehicles. The inverter's output includes harmonics. PSO and TLBO algorithm is used in this paper To find out the best switching angles for a CHBMLI selective harmonic elimination. In order to get the appropriate fundamental component and get rid of low-order harmonics, this project solves nonlinear equations. These algorithms can produce the most effective triggering pulses to minimize Overall Harmonic Distortion(THD) and Selected Harmonics. If the harmonics are removed, the inverter's output will be improved. MATLAB is used to implement and compared both optimization techniques usingthe seven-level inverter. [ABSTRACT FROM AUTHOR]

In order to solve the ill-posed problem in super-resolution image reconstruction, this paper proposes an adaptive regularization way use sparse representation. We build a new L1/2 non-convex optimization model and apply reweighted L2 Norm for the adaptive algorithm in this paper. Experimental results show the significant effect in denoising and preserving edge details. It outperforms some traditional methods in the value of peak signal to noise ratio and structural similarity [ABSTRACT FROM AUTHOR]

This paper attempts to compare the two socio-inspired based metaheuristics algorithms which were introduced recently. These metaheuristics optimizations and search techniques "Election algorithm, and Election Campaign Optimization Algorithm" were inspired by socio-political processes of human ideologies. This paper gives an overview of these algorithms with respect to their control parameters, components, intensification and diversification as well as their applications. It also makes a simple comparison between the two metaheuristics algorithms in terms of quality of solution, ease of finding correct control parameters and further explored their strengths and limitations as well as their superiority over other metaheuristics algorithms. [ABSTRACT FROM AUTHOR]

Optimizing modes of the modern power system on active power is a complex problem of nonlinear mathematical programming. Despite the existence of many methods and algorithms for solving this problem, the issues of their improvement in the direction of correctly taking into account the factors that affect the regime remains an important problem. One of such factors is functional inequality constraints. The paper presents a new algorithm for optimising the modes of power system, taking into account losses in networks and functional constraints in the form of inequalities on power flows of controlled power transmission lines (PTL). A characteristic feature of the algorithm is taking into account the losses in branches by transferring them to the nodes of the electrical network. The algorithm's efficiency is investigated by the example of optimization of the mode of a complex electric power system with four thermal power plants participating in the optimization, ten PTL, in two of which the active power flows are controlled. In this case, the power flows in the controlled lines are determined by linearized formulas using the power distribution coefficients of the nodes. The economic effect on the total fuel consumption due to the use of the proposed algorithm is 0.45%. [ABSTRACT FROM AUTHOR]

OCR post-processing is an important step to improve OCR text accuracy. It includes two main tasks, error detection and error correction. Hill climbing algorithm is a heuristic search method used for solving optimization problems. In this paper, we present a novel OCR error correction approach using an adapted version of the Hill climbing algorithm. Correction candidates of OCR errors are explored by random character edits and evolved with the Hill climbing. The character edit patterns are obtained from the training data. The proposed model is evaluated on the benchmark dataset in the OCR post-correction competition of the International Conference on Document Analysis and Recognition 2017. It is shown that our model outperforms various baseline approaches in the competition. In addition, the randomness of the proposed algorithm is analyzed to verify its stability under parameter configurations. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) process has a wide capability to generate complex shapes on different materials which are occasionally difficult to cut. Its ability to machine a variety of materials makes it an extensively accepted non-traditional machining process in modern day manufacturing sector. Thus, selection of the optimal input parameters for an ECM process is crucial for its efficient utilization. In this paper, a comparative analysis is made among four metaheuristics, i.e. firefly algorithm (FA), differential evolution (DE), ant colony optimization (ACO) algorithm and teaching-learning-based optimization (TLBO) algorithm to discover the optimal values of various control parameters for an ECM process. Dimensional inaccuracy, tool life and material removal rate are the three responses considered which are subjected to temperature, choking and passivity constraints. The TLBO algorithm shows the best performance among the others without violating any of the constraints. The paired t-test is also performed to prove the efficacy of TLBO algorithm over the other optimization techniques. The results derived from these algorithms are finally compared with those obtained by the past researchers using other optimization methods for both single and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, we propose EasyBO, an Efficient Asyn-chronous Batch Bayesian Optimization approach for analog circuit synthesis. In this proposed approach, instead of waiting for the slowest simulations in the batch to finish, we accelerate the optimization procedure by asynchronously issuing the next query points whenever there is an idle worker. We introduce a new acquisition function which can better explore the design space for asynchronous batch Bayesian optimization. A new strategy is proposed to better balance the exploration and exploitation and guarantee the diversity of the query points. And a penalization scheme is proposed to further avoid redundant queries during the asynchronous batch optimization. The efficiency of optimization can thus be further improved. Compared with the state-of-the-art batch Bayesian optimization algorithm, EasyBO achieves up to 7.35 x speed-up without sacrificing the optimization results. [ABSTRACT FROM AUTHOR]

Topology optimization, although well recognized is still widely developed. It has gained recently more attention since large computational ability become available for designers. This process is stimulated simultaneously by variety of emerging, innovative optimization methods. It is observed that traditional gradient-based mathematical programming algorithms, in many cases, are replaced by novel and e cient heuristic methods inspired by biological, chemical or physical phenomena. These methods become useful tools for structural optimization because of their versatility and easy numerical implementation. In this paper engineering implementation of a novel heuristic algorithm for minimum compliance topology optimization is discussed. The performance of the topology generator is based on implementation of a special function utilizing information of compliance distribution within the design space. With a view to cope with engineering problems the algorithm has been combined with structural analysis system Ansys. [ABSTRACT FROM AUTHOR]

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively. [ABSTRACT FROM AUTHOR]

The paper presents an application of a novel concept of graph - the Incompatibility and Complement Graph - to state assignment for asynchronous FSMs. A specific feature of the graph is that it contains two kinds of edges: connecting mutually incompatible nodes, and connecting mutually complementing nodes. The graph can be useful in certain class of optimization problems, in which compatibility of bit patterns both in the true, and in the complemented form has to be analyzed. An example of such a problem is covering analysis in state assignment for asynchronous FSMs. The paper presents a simple example explaining the method. Appropriate algorithms for the graph building and coloring are also presented. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a stochastic computational technique for finding optimal regions of complex search spaces. The method was inspired by social behavior of organism and it belongs to the evolutionary computation (EC) to the group of swarm intelligence (SI). It has a few parameters to adjust and the method is easy to implement, which are the main advantages. The main aim of this paper is to find out, if limitation of the search space influences the optimization process. In this paper, we compare two modifications of PSO algorithm - variant using the parameter of inertia weight and variant with constriction factor. Both variants are solved separately for constrained and unconstrained space. Constrained area is when the boundaries are clear district, unconstrained is when particles can cross the boundaries. For comparison, eleven benchmark functions which were prepared for the special session on real-parameter optimization of CEC 2005 were used. Differences in results between constrained and unconstrained area is presented in this paper. It was found, that constrained area of search space gives better results. In additional, variant of PSO algorithm with linearly decreasing inertia weigh is better for chosen optimization problem. [ABSTRACT FROM AUTHOR]

The Broyden family method for unconstrained optimization is known as one of the most efficient method in solving unconstrained optimization problem. Step size and search direction play the most important role to the convergence of the Broyden family method. Step size is proved to be successful method to solve the problem; however, it is definite to solve some complicated optimization problems. In this paper, we introduced the alternative procedure in determining the step size. This alternative procedure will be applied in the hybrid Broyden method. The numerical results based on number of iterations, number of gradient evaluation and number of function evaluation are given at the end of the paper. [ABSTRACT FROM AUTHOR]

In this paper multi-Tasking operation-based power converter is designed with 3 sources that offers stability and better, control Which essential in conventional converters power distribution. The design of converter covers operation of Bi-direction DC-DC, Bi-direction DC-AC and AC-DC which is applied to electrical vehicle charging converter operation. The operation of proposed system is control using pulse given to breaker to achieve the required operation. The pulse signals given to circuit breaker and MOSFET switches is accordingly, and it is controlled by teaching and learning optimization technique. TLBO technique is proposed to solve constrained in design optimization problem with the function parameters designed with variable objective function for the desired operation to obtain optimum value of objective function. The proposed system output made to give to EV charging port and battery further to test stability of system. Buck and boost operation, ac to dc, dc to ac modes is operating mode of MULTI-TASKING converter with TLBO algorithm incorporates using MATLAB designed in Simulink environment. Experimental results suggest the proposed converter model with controller better response for EVs vehicles based on solar PV source charging. [ABSTRACT FROM AUTHOR]

This paper aims at developing a multi-objective nonlinear optimization algorithm applied to coordinated motorway ramp metering. The multi-objective function includes two components: traffic and safety. Off-line simulation studies were performed on A4 France Motorway including 4 on-ramps. [ABSTRACT FROM AUTHOR]

Any large functioning system consists of equipment that needs to be repaired during its lifetime. The methods of mathematical programming are used to formalize the optimization problem of preventive equipment repair planning in this paper. The method is used to find the annual optimal plan of preventive equipment repair. The goal of such plan is the uniform distribution of repair works by the months. It is supposed, that the deviation from the standard plan of repair works can not be more than one month. An algorithm to find the optimal plan of repair works is provided. A numerical example is given. [ABSTRACT FROM AUTHOR]

The conjugate gradient (CG) and Davidon, Fletcher and Powell (DFP) method are both well known solvers for solving unconstrained optimization problems. In this paper, we proposed a new hybrid DFP-CG method and compared with the ordinary DFP method in terms of number of iteration and CPU times. Numerical results show that the new algorithm is more efficient compared to the ordinary DFP method and proven to posses both sufficient descent and global convergence properties. [ABSTRACT FROM AUTHOR]

Customer positioning has always been a problem that enterprises focus on. In this paper, FCM clustering algorithm is used to cluster customer groups. However, due to the traditional FCM clustering algorithm, which is susceptible to the influence of the initial clustering center and easy to fall into the local optimal problem, the short board of FCM is solved by the gray optimization algorithm (GWO) to achieve efficient and accurate handling of a large number of retailer data. [ABSTRACT FROM AUTHOR]

This paper provides a metaheuristic-based of classification rules. Indeed, The classification problem is a data mining task which belongs to the NP-Complete problem class. This is a combinatorial problem which may find good solutions using metaheuristics approaches. We are interested in adapting the Bee Swarm Optimisation (BSO) algorithm which is relatively a new metaheuristic. Our aim is to discover a classification model. This BSO approach already gave some interesting solutions for the well-known SAT problem and we will show the efficiency of the method for the Problem of the Extraction of Classification Rules also. [ABSTRACT FROM AUTHOR]

This paper speaks to the changed Tropos objective model for handling the danger related with the product advancement cycle. Here the modification is done by the hybrid optimization algorithm. The tropos model will be improved by selecting the optimal candidate solution by using Hybrid Gravitational Search Algorithm – Particle Swarm Optimization algorithm. The proposed approach characterizes a strategy to dissect the relationship between the hubs of each layer to assess their odds of raising the dangers. Here the risks are prioritized and which are identified by the improved Tropos method. The obtained results show that, the proposed optimization-based goal model has delivered better performance in terms of time and memory requirement when compared with the normal model and also the risk and cost of the proposed model is proved to be more efficient than existing model. [ABSTRACT FROM AUTHOR]

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms. [ABSTRACT FROM AUTHOR]

In view of the needs of the vehicle unit, this paper has carried on the research to the sensor distribution question. In the case of fully taking into account the information such as target identity and target priority, the mathematical model of optimal sensor allocation is established based on the maximum information gain criterion, and then the population-based incremental learning algorithm is used to solve. Finally, the validity of the method is verified by solving the problem of sensor assignment problem of the vehicle unit. [ABSTRACT FROM AUTHOR]

The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problem in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimizing proble3ms. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension. [ABSTRACT FROM AUTHOR]

Consideration was given to the problem of relative positioning with the use of alternating magnetic field. The problem was shown to be solvable using a system of three magnetic dipoles emitting magnetic field at different frequencies. Ambiguity of its solution was discussed, and ways to resolve it were proposed. An algorithm for relative positioning based on the solution of this problem was proposed. Error equations of the positioning algorithm and the results of their analysis were presented. Calibration procedures, improving accuracy of relative positioning, are also considered. One of these procedures, based on nonlinear optimization techniques, was presented in greater details. The paper was supplied with several numerical examples demonstrating effectiveness of proposed algorithms in relative positioning. [ABSTRACT FROM AUTHOR]

The paper studies constructive tools of optimizing the 3D printing process in SLS additive production technology. We develop NLP model and solution algorithm that allow us simultaneously printing a large number of products to maximize the volume of the working chamber of a 3D printer, taking into account technological requirements. Our approach allows to minimize the time and cost of creating products by significantly reducing the number of printing cycles. [ABSTRACT FROM AUTHOR]

In case of performance critical applications programmers are often forced to write code at a low abstraction level. This leads to programs that are hard to develop and maintain because the program text is mixed up by low level optimization tricks and is far from the algorithm it implements. Even if compilers are smart nowadays and provide the user with many automatically applied optimizations, practice shows that in some cases it is hopeless to optimize the program automatically without the programmer's knowledge. A complementary approach is to allow the programmer to fine tune the program but provide him with language features that make the optimization easier. These are language abstractions that make optimization techniques explicit without adding too much syntactic noise to the program text. This paper presents such language abstractions for two well-known optimizations: bitvectors and SIMD (Single Instruction Multiple Data). The language features are implemented in the embedded domain specific language Feldspar which is specifically tailored for digital signal processing applications. While we present these language elements as part of Feldspar, the ideas behind them are general enough to be applied in other language definition projects as well. [ABSTRACT FROM AUTHOR]

In a Multicore architecture, each package consists of large number of processors. This increase in processor core brings new evolution in parallel computing. Besides enormous performance enhancement, this multicore package injects lot of challenges and opportunities on the operating system scheduling point of view. We know that multiagent system is concerned with the development and analysis of optimization problems. The main objective of multiagent system is to invent some methodologies that make the developer to build complex systems that can be used to solve sophisticated problems. This is difficult for an individual agent to solve. In this paper we combine the AMAS theory of multiagent system with the scheduler of operating system to develop a new process scheduling algorithm for multicore architecture. This multiagent based scheduling algorithm promises in minimizing the average waiting time of the processes in the centralized queue and also reduces the task of the scheduler. We actually modified and simulated the linux 2.6.11 kernel process scheduler to incorporate the multiagent system concept. The comparison is made for different number of cores with multiple combinations of process and the results are shown for average waiting time Vs number of cores in the centralized queue. [ABSTRACT FROM AUTHOR]

This paper analyzes the current situation of the intrusion detection system, which is the basis to put forward that data mining technology is to be applied to the intrusion detection system in terms of the problems of the traditional intrusion detection system. Meanwhile, the paper designs the intrusion detection model of data mining. With the study on intrusion detection and data mining, the algorithm of classical relation with clustering in the characteristics on intrusion detection system is improved and optimized. [ABSTRACT FROM AUTHOR]

The paper introduces a bio-inspired approach to solve the route allocation problem (RAP) in the transportation networks. The approach extends a well-known meta-heuristics algorithm with the real life constraints that are dealt with in the scheduling process (i.e. the uniform distribution of routes diversity for vehicles, the average distance travelled in a month, the driver’s rest between subsequent trips etc.). The paper is focusing on the engineering aspects of employing bio-inspired algorithms (which proved to have near-optimal results for toy-like problems) to a real-life application domain. The approach proved to be capable of preserving the software components (agents) to the complexity and dynamics of the situation when the RAP requires incremental extensions of constraints to reflect the traffic conditions in the transportation network. [ABSTRACT FROM AUTHOR]

Data Deduplication is defined as an elimination process of redundant duplicated data; this process is stratified by using a unique value that been represented by a chunk of data, which is referenced by the original file that contains this chunk. Data Deduplication techniques have been mainly applied in the cloud-based systems in order to decrease the space size of the data storage, and enhance the connection bandwidth. In this paper, we have introduced a data deduplication optimization technique that been applied to the data storage of cloud-based systems. The proposed technique optimizes data deduplication by implementing Source-Based and In-Line based techniques. The Source-Based method is stratified at the source that contains the data, on the other hand, the In-Line method is stratified at the RAM that contains the data momentarily before the writing process of the I/O. Moreover, the proposed technique applies a Content-Based chunking algorithm with Variable Chunking utilization by using Rabin Karp Rolling Hash (RKRH). RKRH is a data chunking algorithm that breaks data files into different segments sizes. Generally, the proposed technique process is based on calculating the hash value of data chunk which considered as a fingerprint. Afterward, the chunk availability process is applied in order to identify the existence of this chunk in the storage; therefore, if this chunk does not exist in the storage a reference to this chunk is added and store the hash value as a key in the storage. The proposed technique includes data chunk compression to reduce the data redundancy in the same chunk. Practically, the proposed technique provides a data deduplication ratio of 33 percent and an average upload latency of five seconds. Finally, the proposed approach utilized with any data file type as a byte stream. [ABSTRACT FROM AUTHOR]

It’s crucial for process engineers to determine optimal value and combination of process parameters in the design of sheet metal forming. The multi-objective genetic algorithm (MOGA) based on Pareto approach and numerical simulation codes were integrated in this paper to fulfill the optimal formability in the sheet metal forming. Three objective functions of local formability on fracture, wrinkling and insufficient stretching were presented based on the strains state at the end of the forming process on the Forming Limit Diagram. By using Pareto-based MOGA, the optimal global formability which represents the trade-off between different local formability was decided. For the efficiency and accuracy of optimization procedure, both inverse and incremental finite element analysis were used to evaluate the value of objective functions. This method was applied to a complex engineering optimization problem: an engine hood outer panel, the optimal blank holder force and draw bead restraining forces were determined to satisfy the given objective functions for the forming of the auto body panels. The approach proposed in this paper has been shown to be a powerful tool than manual numerical simulation procedure. © 2005 American Institute of Physics [ABSTRACT FROM AUTHOR]

Presented paper performs an analysis of quadrature signal processing algorithms for high demanding laser interferometry applications. Careful signal processing is required to minimize nonlinearities which come from optical path and components' imperfections, and reduce overall instrumental error. Paper focuses on algebraic fits, because implementation for real time systems was a main requirement. The most demanding applications are stationary measurements where the position slightly fluctuates in the range below one fringe period. Therefore, analysis was performed for samples that were spread along a few milliradians of a full circle. [ABSTRACT FROM AUTHOR]

As interconnects dominate circuit performance in modern chip designs, placement becomes an essential stage in optimizing timing. Recent timing-driven placement (TDP) techniques focus mainly on optimizing late slack rather than early slack. This paper presents a TDP algorithm to improve the early slack while preserving an optimized late slack. The preservation is achieved by accurately predicting optimal Steiner tree topologies after each move in our TDP algorithm. An optimality-preserving pruning scheme for each move is proposed to speed up the optimization process, without sacrificing the solution quality. Experimental results show that our algorithm can substantially improve the early slacks and the overall quality scores of the top-2 winning placers of the 2015 ICCAD Incremental Timing-Driven Placement Contest, while preserving their late slacks. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the 'erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature. [ABSTRACT FROM AUTHOR]

Power wiring is the cornerstone of urban expansion and social progress. In recent decades, the traditional route routing method takes the first place, that is, to minimize the construction pressure without considering the cost first. However, this method is often restricted by many unexpected factors, and it often takes a long time to choose an appropriate path, which seriously affects the construction progress. Combined with the intelligent algorithm and the understanding of the real situation, we designed the power wiring system (PW system) and cabling optimization system (CO system). Through computer programming, the actual problems are digitized, and the actual problems are transformed into virtual points. The swarm intelligence algorithm is used to generate the optimal path through the computer. At the same time, the use of the computer can compare and choose the problems and factors that are difficult to be considered at the same time in the traditional wiring. Moreover, the flexibility and convenient manipulation of the computer also create convenient conditions for the later maintenance, which can effectively improve the efficiency of power wiring and reduce the wiring cost. Through the research on intelligent cabling, it aims to provide more effective methods for power cabling and help solve the problem of power demand arising from the rapid development of society. [ABSTRACT FROM AUTHOR]

In this paper, the global minimization problem of a multi-dimensional black-box Lipschitzian function is considered. In order to pass from the original Lipschitz multi-dimensional problem to a univariate one, an approach using space-filling curves to reduce the dimension is applied. The method does not use derivatives and, at each iteration, works with a set of estimates of the Hölder constant of the reduced one-dimensional problem. A two-phase technique is applied to accelerate the search of the global minimum. Numerical experiments carried out on several hundreds of test functions show a promising performance of the discussed algorithm in comparison with its direct competitors. [ABSTRACT FROM AUTHOR]

We aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI). [ABSTRACT FROM AUTHOR]

This paper summarizes the collaborative work done by CENER, Fraunhofer ISE and CRS4 within the scope of the STAGE-STE project related to heliostat field generation algorithms and their application to small heliostats (<10 m²). Radially staggered heliostat field layouts have been commonly applied to real solar fields with known results. However, their use in conjunction with small heliostats or the use of brand new ways for heliostat allocation, such natural pattern based algorithms, is still unknown. Herein, the most promising heliostat field generation algorithms are studied and compared, in annual optical efficiency terms, for different scenarios. These efficiencies correspond to the best heliostat layouts found by the optimization process that each generation algorithm can create, for three scenarios. Results show that the fields from the selected algorithms lead to similar optical efficiencies. The slight differences are not enough evidence to nominate one of the algorithms as the best choice, taking into account the inherent error of the simulation tools, the optimization process and further requirements needed in commercial applications (e.g. access paths) not coped in this study. [ABSTRACT FROM AUTHOR]

Assembly sequence planning (ASP) plays an important role in manufacturing because it determines the investment on the assembly facilities. Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. Besides the algorithm efficiency, another factor that directly contributed to the quality of the solution is how the problem is modelled. According to the existing research, the most accurate approach to represent the ASP is to model base on the part of product. However, this approach resulting in the large number of assembly sequence possibility, and finally make the optimization process become harder. This paper proposed a simpler version of part-based representation using the base part concept. In the proposed model, the base part for the product is defined prior to the establishment of the precedence constraint. This approach has contributed to a smaller search space that resulting in better chance to obtain optimum solution during the optimization. The proposed ASP modelling approach however limited the number of possible solution, since the base part was fixed prior to the optimization. [ABSTRACT FROM AUTHOR]

This paper proposes a novel parallelization of ABC Algorithm to solve Short-term Hydro-Thermal Scheduling (SHTS) problem. Parallelization is a novel way of improving the convergence of the algorithm. In case of normal ABC algorithms accelerating operation or modification can be used for better convergence and diversity. But with large number of variables the non-linearity of the search space increases and these modifications are not sufficient to give enough diversity in the crowds of swarms for finding better solutions. However, parallelization of the algorithm increases the diversity in solutions and the search capability. The performance of the proposed algorithm is experimented on two short term multi reservoir cascaded hydrothermal systems. First test case is moderately large while the second test case is a considerably large one. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions. [ABSTRACT FROM AUTHOR]

Groupwise non-rigid image alignment is a difficult non-linear optimisation problem, involving many parameters and often large datasets. Previous methods have explored various metrics and optimisation strategies. Good results have been previously achieved with simple metrics, but requiring complex optimisation, often with many unintuitive parameters that require careful tuning for each dataset. In this paper we restructure the problem to use a simpler, iterative optimisation algorithm, with very few free parameters. We demonstrate how to incorporate a stiffness constraint and how to tune the few remaining parameters. Results show that the method reliably aligns various datasets and demonstrates efficiency in terms performance and reduction of the computations. [ABSTRACT FROM AUTHOR]

Real-life optimization problems consist of various conflicting optimization problems. Due to the interdependency of these sub-optimization problems, their solution is more difficult than single optimization problems. For this reason, most of the real-life optimization problems are considered multi-objective optimization problems by nature, and different methods and approaches are applied. In this paper, a recent multi-objective optimization problem, the bi-objective travelling thief problem (BiTTP) was studied. Two state-of-the-art algorithms, ALNS and NSGA-II, were hybridized with certain local search methods and applied on six problem instances of Polyakovskiy's problem benchmark set. As a result, the performances of these two algorithms were evaluated and a new set of Pareto front sets were generated for the related benchmark instances. [ABSTRACT FROM AUTHOR]

Exploration of an unknown environment by autonomous mobile robot is a fundamental concern in mobile robotics. Today most of the mobile robots are powered by batteries so their energy and operation times are limited. Therefore, how to minimize energy consumption and obstacle avoidance becomes an important problem. Frontier-based method is known to be most efficient for robot exploration system. In this paper, we propose a novel energy efficient platform based on the notion of frontiers for mobile robot. The robot selects the next frontier to visit based on the robot's current direction and relative location of frontier cells. Our frontier selection method can reduce duplicate coverage and thus save energy and reduce the distance also. Distance to frontier are computed using energy efficient A* algorithm. We estimate the energy consumption and choose the most energy-efficient route to move to that frontier considering energy of stop and turnings. We tested the algorithm on various types of environment maps. The experiments were conducted assuming equal velocity for the robot during whole exploration. All paths generated were optimal in terms of energy consumption and turns. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner. [ABSTRACT FROM AUTHOR]

The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detection. Recently, the ACO was used in fundus images to detect edges, and therefore, to segment the OD and other anatomical retinal structures. We present an algorithm for the detection of OD in the retina which takes advantage of the Gabor wavelet transform, entropy and ACO algorithm. Forty images of the retina from DRIVE database were used to evaluate the performance of our method. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a fast and robust real vector optimizer. This paper applies DE to discrete problems by converting a real chromosome to an integer chromosome and then decompress to a binary chromosome using LZW algorithm. Experimental result shows that this approach is better than the previous work and the evolution time is very fast. [ABSTRACT FROM AUTHOR]

In open vehicle routing problems, the vehicles are not required to return to the depot after completing service. In this paper, we present the first exact optimization algorithm for the open version of the well-known capacitated vehicle routing problem (CVRP). The strategy of releasing nonbasic variables from their bounds, combined with the 'active constraint' method and the notion of superbasics, has been developed for efficiently requirements; this strategy is used to force the appropriate non-integer basic variables to move to their neighborhood integer points. A study of criteria for choosing a nonbasic variable to work with in the integerizing strategy has also been made. [ABSTRACT FROM AUTHOR]

Ductile damage mechanics is essential to predict failure during cold metal forming applications. Several damage models can be found in the literature. These damage models are coupled with the mechanical behavior so as to model the progressive softening of the material due to damage growth. However, the identification of damage parameters remains an issue. In this paper, an inverse analysis approach is set-up to identify ductile damage parameters, based on different kind of mechanical tests and observables. The Lemaitre damage model is used and damage is coupled with the material behavior. The Efficient Global Optimization (EGO) method is used in a parallel environment. This global algorithm based on kriging meta-model enables the identification of a set of damage parameters based on experimental observables. Global and local observables are used to identify these parameters and a special attention is paid to the computation of the cost function. Finally, an identification procedure based on displacement field measurements is presented and applied for damage parameters identification. [ABSTRACT FROM AUTHOR]

In the last years a great interest in optimization algorithms aimed to design forming processes was demonstrated by many researches. Proper design methodologies to reduce times and costs have to be developed mostly based on computer aided procedures. Response surface methods (RSM) proved their effectiveness in the recent years also for the application in sheet metal forming aiming to reduce the number of numerical simulations. Actually, the main drawback of such method is the number of direct problem to be solved in order to reach good function approximations. A very interesting aspect in RSM application regards the possibility to build response surfaces basing on moving least squares approximations (MLS) by utilizing a moving and zooming region of interest. In this paper, MLS is utilized to solve an optimization problem for a complex sheet metal forming process. The influence on the optimization results was analyzed basing on MLS peculiarities. In particular, the analysis was carried out studying the effect of weighting functions shape and of the points belonging to the design of experiments. [ABSTRACT FROM AUTHOR]

The dual Schrödinger equation is defined as replacing the imaginary number i by -1 in the original one. This paper shows that the dual equation shares the same stationary states as the original one. Different from the original one, it explicitly defines a dynamic process for a system to evolve from any state to lower energy states and eventually to the lowest one. Its power as a global optimization algorithm might be used by nature for constructing atoms and molecules. It shall be interesting to verify its existence in nature. [ABSTRACT FROM AUTHOR]

In the present paper an attempt has been made to place the distributed generation at an optimal location so as to improve the technical as well as economical performance. Among technical issues the sag performance and the loss have been considered. Genetic algorithm method has been used as the optimization technique in this problem. For sag analysis the impact of 3-phase symmetrical short circuit faults is considered. Total load disturbed during the faults is considered as an indicator of sag performance. The solution algorithm is demonstrated on a 34 bus radial distribution system with some lateral branches. For simplicity only one DG of predefined capacity is considered. MATLAB has been used as the programming environment. [ABSTRACT FROM AUTHOR]