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The purpose of this paper is to present a hierarchical circuit synthesis system with a hybrid deterministic local optimization – multi‐objective genetic algorithm (DLO‐MOGA) optimization scheme for system‐level synthesis.

The use of a local optimization with a deterministic algorithm based on linear equations which is computationally efficient and improves the feasibility of designs, allows reduction in the number of MOGA generations required to achieve convergence.

This approach reduces the total number of simulation iterations required for optimization. Reduction in run time enables use of full transistor‐level models for simulation of critical system‐level sub‐blocks. Consequently, for system‐level synthesis, simulation accuracy is maintained. The approach is demonstrated for the design of pipeline analog‐to‐digital converters on a 0.35 μm process.

The use of a hybrid DLO‐MOGA optimization approach is a new approach to improve hierarchical circuit synthesis time while preserving accuracy.

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum (COOBBO) algorithm.

The improvement measures tested in this paper include different initialization approaches, crossover approaches, local optimization approaches, and greedy approaches. Eight well-known traveling salesman problems (TSP) are employed for performance verification. Four comparison criteria are recoded and compared to analyze the contribution of each modified method.

Experiment results illustrate that the combination model of “25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy” may be the best choice of all when considering both the overall algorithm performance and computation overhead.

When solving TSP with varying scales, these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees. And an appropriate combination model may make the fullest possible contribution.

The purpose of this paper is to present a methodology for the evaluation of transport aircraft fuselages constructed in a semi-monocoque design.

A fuselage barrel was computed statically and dynamically using finite element methods. Static analysis was conducted using a global/local approach in which the section loads of the global model were used as load introduction in the local model. Subsequently, a crash analysis was performed, and the results from both disciplines were evaluated by either an optimization or parameter variation algorithm.

The presented process chain has been developed for use in preliminary design stages to assess aircraft configurations with regard to statics and dynamics. Parameter variation and optimization were conducted, proving functionality of the methodology.

In this early stage of methodology development only one exemplary static load case is considered and the fuselage design is limited to a constant section.

The presented process chain shows an approach to couple different disciplines to reduce the analysis time in aircraft preliminary design phase.

This methodology couples static design and crashworthiness aspects at an early design stage to avoid time- and cost-intensive redesign in subsequent detailed design stages. The process chain introduced in this paper uses a parameterized approach, making this methodology applicable for each fuselage in semi-monocoque design.

Gives a bibliographical review of the finite element meshing and remeshing from the theoretical as well as practical points of view. Topics such as adaptive techniques for meshing and remeshing, parallel processing in the finite element modelling, etc. are also included. The bibliography at the end of this paper contains 1,727 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1990 and 2001.

This paper aims to use the redundancy of a 7-DOF (degree of freedom) serial manipulator to solve motion planning problems along a given 6D Cartesian tool path, in the presence of geometric constraints, namely, obstacles and joint limits.

This paper describes an explicit expression of the task submanifolds for a 7-DOF redundant robot, and the submanifolds can be parameterized by two parameters with this explicit expression. Therefore, the global search method can find the feasible path on this parameterized graph.

The proposed planning algorithm is resolution complete and resolution optimal for 7-DOF manipulators, and the planned path can satisfy task constraint as well as avoiding singularity and collision. The experiments on Motoman SDA robot are reported to show the effectiveness.

This algorithm is still time-consuming, and it can be improved by applying parallel collision detection method or lazy collision detection, adopting new constraints and implementing more effective graph search algorithms.

Compared with other task constrained planning methods, the proposed algorithm archives better performance. This method finds the explicit expression of the two-dimensional task sub-manifolds, so it’s resolution complete and resolution optimal.

To describe the tactics that buyers often use to avoid unfavorable purchase price variance (PPV) and identify alternate approaches that will improve purchasing performance and also help achieve company objectives.

Descriptive: presents for the first time 12 dysfunctional tactics used by buyers of industrial goods use to avoid unfavorable PPV.

The tactics are shown to increase costs rather than decrease costs and lead to organizational dysfunction. Findings are broadly applicable to large corporations that use legacy software systems or newer enterprise requirement planning (ERP) software systems to track purchasing costs and transactions, and also have a strong management focus on price‐based purchasing performance.

Findings are limited to organizations that measure the success of purchasing and supply management activities using price‐based metrics.

Should propel managers to identify alternative metrics or processes for managing purchasing performance, reduce system‐wide costs, and improve day‐to‐day work in purchasing organizations.

This paper will be helpful to academics researching operational or behavioral aspects of purchasing, practitioners managing supply chains, auditors assessing the integrity of material cost reporting and management controls, and persons concerned about ethics in business.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

Simulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.

In this work, a comparative analysis of heuristic simulation optimization methods (genertic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed. Further, a novel simulation annealing-based heuristic approach is proposed for critical infrastructure.

A small scale network of 50–100 nodes shows that genetic simulation optimization with multi-criteria and multi-dimensional features performs better as compared to other simulation optimization approaches. Further, a minimum of 3.4 percent and maximum of 16.2 percent improvement is observed in faster route identification for small scale Internet-of-things (IoT) networks with simulation optimization constraints integrated model as compared to the traditional method.

In this work, simulation optimization techniques are applied for identifying optimized Quality of service (QoS) parameters for critical infrastructure which in turn helps in improving the network performance. In order to identify optimized parameters, Tabu search and ant-inspired heuristic optimization techniques are applied over QoS parameters. These optimized values are compared with every monitoring sensor point in the network. This comparative analysis helps in identifying underperforming and outperforming monitoring points. Further, QoS of these points can be improved by identifying their local optimum values which in turn increases the performance of overall network. In continuation, a simulation model of bus transport is taken for analysis. Bus transport system is a critical infrastructure for Dehradun. In this work, feasibility of electric recharging units alongside roads under different traffic conditions is checked using simulation. The simulation study is performed over five bus routes in a small scale IoT network.

The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.

The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.

Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.

The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.

The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.

The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.